CAPSTONE PROJECT - CAR DETECTION

COMPUTER VISION PROJECT- PROJECT BY GROUP 8
In [ ]:

Milestone 1

Step 1: Import the data
In [241]:
import numpy as np
import plotly.express as px
import os
import zipfile
import cv2
from PIL import Image
import ipywidgets as widgets
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from collections import defaultdict
from io import StringIO
import random
import tensorflow as tf
from sklearn.metrics import accuracy_score
import pandas as pd
import seaborn as sns
from IPython.display import display
import pickle
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import ResNet50, VGG19, MobileNet, EfficientNetB0, MobileNetV2
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, Concatenate, Conv2D, Input, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D
from tensorflow.keras.models import Model, load_model, Sequential
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix
import tkinter as tk
from tkinter import filedialog


from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import keras.backend as K
import keras.layers as KL
import keras.models as KM
from keras.applications.resnet50 import ResNet50
from keras.optimizers import Adam
from keras.losses import categorical_crossentropy
from keras.callbacks import ModelCheckpoint

%matplotlib inline
In [2]:
car_data_zip_path = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/Car+Images.zip'
annotation_zip_path = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/Annotations.zip'

destination_folder = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites'

if not os.path.exists(destination_folder):
    os.makedirs(destination_folder)

with zipfile.ZipFile(car_data_zip_path, 'r') as car_data_zip:
    car_data_zip.extractall(os.path.join(destination_folder, 'car_data_extracted'))

with zipfile.ZipFile(annotation_zip_path, 'r') as annotation_zip:
    annotation_zip.extractall(os.path.join(destination_folder, 'annotations_extracted'))

print("Data extracted and stored in the destination folder.")

Data extracted and stored in the destination folder.
In [3]:
carmodel_file = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/Car+names+and+make.csv'
car_data = pd.read_csv(carmodel_file)
print("Car Data:")
print(car_data.head())

Car Data:
  AM General Hummer SUV 2000
0        Acura RL Sedan 2012
1        Acura TL Sedan 2012
2       Acura TL Type-S 2008
3       Acura TSX Sedan 2012
4  Acura Integra Type R 2001
Step 2: Map training and testing images to its classes
In [4]:
def process_images(folder_path):
    data = []

    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
                car_name = os.path.basename(root)
                car_model = car_name.split()[-1]
                car_model_1 = ' '.join(car_name.split()[:-1])
                image_name = file
                image_location = os.path.join(root, file)

                data.append({
                    'carName': car_name,
                    'carModel': car_model,
                    'carModel_1': car_model_1,
                    'Image Name': image_name,
                    'Image Location': image_location
                })

    return data

# Defining the path to the main folder containing subfolders with images
Train_image_folder = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Train Images'

# Processing images recursively
Train_data = process_images(Train_image_folder)

# Creating a DataFrame
Train_data_df = pd.DataFrame(Train_data)

# Displaying the final DataFrame with image locations
Train_data_df.head()
Out[4]:
carName carModel carModel_1 Image Name Image Location
0 Acura Integra Type R 2001 2001 Acura Integra Type R 00198.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
1 Acura Integra Type R 2001 2001 Acura Integra Type R 00255.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
2 Acura Integra Type R 2001 2001 Acura Integra Type R 00308.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
3 Acura Integra Type R 2001 2001 Acura Integra Type R 00374.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
4 Acura Integra Type R 2001 2001 Acura Integra Type R 00878.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
In [5]:
def process_images(folder_path):
    data = []

    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
                car_name = os.path.basename(root)
                car_model = car_name.split()[-1]
                car_model_1 = ' '.join(car_name.split()[:-1])
                image_name = file
                image_location = os.path.join(root, file)

                data.append({
                    'carName': car_name,
                    'carModel': car_model,
                    'carModel_1': car_model_1,
                    'Image Name': image_name,
                    'Image Location': image_location
                })

    return data

# Defining the path to the main folder containing subfolders with images
Test_image_folder = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Test Images'

# Processing images recursively
Test_data = process_images(Test_image_folder)

# Creating DataFrame
Test_data_df = pd.DataFrame(Test_data)

# Displaying the final DataFrame
Test_data_df.head()
Out[5]:
carName carModel carModel_1 Image Name Image Location
0 Acura Integra Type R 2001 2001 Acura Integra Type R 00128.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
1 Acura Integra Type R 2001 2001 Acura Integra Type R 00130.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
2 Acura Integra Type R 2001 2001 Acura Integra Type R 00386.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
3 Acura Integra Type R 2001 2001 Acura Integra Type R 00565.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
4 Acura Integra Type R 2001 2001 Acura Integra Type R 00711.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ...
In [ ]:
Lets visualize few images
a : Train Images
In [6]:
Train_data_df.shape
Out[6]:
(8144, 5)
In [7]:
for index, row in Train_data_df.head(5).iterrows():
    image_path = row['Image Location']
    img = Image.open(image_path)
    plt.imshow(img)
    plt.title(f'Image - {row["Image Name"]}\nLocation: {row["Image Location"]}')
    plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
b : Test Images
In [8]:
Test_data_df.shape
Out[8]:
(8041, 5)
In [9]:
for index, row in Test_data_df.head(5).iterrows():
    image_path = row['Image Location']
    img = Image.open(image_path)
    plt.imshow(img)
    plt.title(f'Image - {row["Image Name"]}\nLocation: {row["Image Location"]}')
    plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [ ]:
Step 3: Map training and testing images to its annotations
In [10]:
train_annotations = pd.read_csv("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/annotations_extracted/Annotations/Train Annotations.csv")
test_annotations = pd.read_csv("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/annotations_extracted/Annotations/Test Annotation.csv")
In [11]:
train_annotations
Out[11]:
Image Name Bounding Box coordinates Unnamed: 2 Unnamed: 3 Unnamed: 4 Image class
0 00001.jpg 39 116 569 375 14
1 00002.jpg 36 116 868 587 3
2 00003.jpg 85 109 601 381 91
3 00004.jpg 621 393 1484 1096 134
4 00005.jpg 14 36 133 99 106
... ... ... ... ... ... ...
8139 08140.jpg 3 44 423 336 78
8140 08141.jpg 138 150 706 523 196
8141 08142.jpg 26 246 660 449 163
8142 08143.jpg 78 526 1489 908 112
8143 08144.jpg 20 240 862 677 17

8144 rows × 6 columns

In [ ]:
Lets remane the headings appropriately for the train data!
In [12]:
train_annotations.rename(columns = {'Bounding Box coordinates':'xmin'}, inplace = True)
train_annotations.rename(columns = {'Unnamed: 2':'ymin'}, inplace = True)
train_annotations.rename(columns = {'Unnamed: 3':'xmax'}, inplace = True)
train_annotations.rename(columns = {'Unnamed: 4':'ymax'}, inplace = True)
train_annotations.rename(columns = {'Image class':'Image_class'}, inplace = True)
train_annotations.rename(columns = {'Image Name':'Image Name'}, inplace = True)
train_annotations
Out[12]:
Image Name xmin ymin xmax ymax Image_class
0 00001.jpg 39 116 569 375 14
1 00002.jpg 36 116 868 587 3
2 00003.jpg 85 109 601 381 91
3 00004.jpg 621 393 1484 1096 134
4 00005.jpg 14 36 133 99 106
... ... ... ... ... ... ...
8139 08140.jpg 3 44 423 336 78
8140 08141.jpg 138 150 706 523 196
8141 08142.jpg 26 246 660 449 163
8142 08143.jpg 78 526 1489 908 112
8143 08144.jpg 20 240 862 677 17

8144 rows × 6 columns

In [ ]:
In [13]:
test_annotations
Out[13]:
Image Name Bounding Box coordinates Unnamed: 2 Unnamed: 3 Unnamed: 4 Image class
0 00001.jpg 30 52 246 147 181
1 00002.jpg 100 19 576 203 103
2 00003.jpg 51 105 968 659 145
3 00004.jpg 67 84 581 407 187
4 00005.jpg 140 151 593 339 185
... ... ... ... ... ... ...
8036 08037.jpg 49 57 1169 669 63
8037 08038.jpg 23 18 640 459 16
8038 08039.jpg 33 27 602 252 17
8039 08040.jpg 33 142 521 376 38
8040 08041.jpg 77 73 506 380 32

8041 rows × 6 columns

In [ ]:
Lets remane the headings appropriately for the test data!
In [14]:
test_annotations.rename(columns = {'Bounding Box coordinates':'xmin'}, inplace = True)
test_annotations.rename(columns = {'Unnamed: 2':'ymin'}, inplace = True)
test_annotations.rename(columns = {'Unnamed: 3':'xmax'}, inplace = True)
test_annotations.rename(columns = {'Unnamed: 4':'ymax'}, inplace = True)
test_annotations.rename(columns = {'Image class':'Image_class'}, inplace = True)
test_annotations.rename(columns = {'Image Name':'Image Name'}, inplace = True)
test_annotations
Out[14]:
Image Name xmin ymin xmax ymax Image_class
0 00001.jpg 30 52 246 147 181
1 00002.jpg 100 19 576 203 103
2 00003.jpg 51 105 968 659 145
3 00004.jpg 67 84 581 407 187
4 00005.jpg 140 151 593 339 185
... ... ... ... ... ... ...
8036 08037.jpg 49 57 1169 669 63
8037 08038.jpg 23 18 640 459 16
8038 08039.jpg 33 27 602 252 17
8039 08040.jpg 33 142 521 376 38
8040 08041.jpg 77 73 506 380 32

8041 rows × 6 columns

In [ ]:
Now, Lets merge the dataframe of annotations and image dataframe for the train data!
In [15]:
Train_final_df = pd.merge(Train_data_df, train_annotations, how='inner',left_on='Image Name', right_on='Image Name')
In [16]:
Train_final_df.head(20)
Out[16]:
carName carModel carModel_1 Image Name Image Location xmin ymin xmax ymax Image_class
0 Acura Integra Type R 2001 2001 Acura Integra Type R 00198.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 91 121 574 357 6
1 Acura Integra Type R 2001 2001 Acura Integra Type R 00255.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 29 78 734 396 6
2 Acura Integra Type R 2001 2001 Acura Integra Type R 00308.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 16 136 775 418 6
3 Acura Integra Type R 2001 2001 Acura Integra Type R 00374.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 9 184 740 499 6
4 Acura Integra Type R 2001 2001 Acura Integra Type R 00878.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 101 162 882 650 6
5 Acura Integra Type R 2001 2001 Acura Integra Type R 00898.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 89 68 483 310 6
6 Acura Integra Type R 2001 2001 Acura Integra Type R 01010.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 6 3 932 639 6
7 Acura Integra Type R 2001 2001 Acura Integra Type R 01012.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 58 72 763 529 6
8 Acura Integra Type R 2001 2001 Acura Integra Type R 01255.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 17 80 735 492 6
9 Acura Integra Type R 2001 2001 Acura Integra Type R 01617.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 62 150 543 339 6
10 Acura Integra Type R 2001 2001 Acura Integra Type R 01864.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 64 202 898 574 6
11 Acura Integra Type R 2001 2001 Acura Integra Type R 01911.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 91 175 567 410 6
12 Acura Integra Type R 2001 2001 Acura Integra Type R 02095.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 90 189 1495 820 6
13 Acura Integra Type R 2001 2001 Acura Integra Type R 02605.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 10 34 627 322 6
14 Acura Integra Type R 2001 2001 Acura Integra Type R 02738.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 86 292 1176 639 6
15 Acura Integra Type R 2001 2001 Acura Integra Type R 02911.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 64 240 1508 943 6
16 Acura Integra Type R 2001 2001 Acura Integra Type R 02919.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 42 96 612 353 6
17 Acura Integra Type R 2001 2001 Acura Integra Type R 03265.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 33 150 619 409 6
18 Acura Integra Type R 2001 2001 Acura Integra Type R 03608.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 180 205 538 400 6
19 Acura Integra Type R 2001 2001 Acura Integra Type R 03670.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 146 73 486 236 6
In [17]:
train_annotations.iloc[3677]
Out[17]:
Image Name     03678.jpg
xmin                  66
ymin                  79
xmax                 224
ymax                 168
Image_class          190
Name: 3677, dtype: object
In [18]:
Train_final_df.shape
Out[18]:
(8144, 10)
In [ ]:
Now, Lets merge the dataframne of annotations and image dataframe for the test data!
In [19]:
Test_final_df = pd.merge(Test_data_df, test_annotations,how='inner', left_on='Image Name', right_on='Image Name')
In [20]:
Test_final_df.head(20)
Out[20]:
carName carModel carModel_1 Image Name Image Location xmin ymin xmax ymax Image_class
0 Acura Integra Type R 2001 2001 Acura Integra Type R 00128.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 122 149 743 455 6
1 Acura Integra Type R 2001 2001 Acura Integra Type R 00130.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 27 74 774 449 6
2 Acura Integra Type R 2001 2001 Acura Integra Type R 00386.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 86 184 723 425 6
3 Acura Integra Type R 2001 2001 Acura Integra Type R 00565.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 15 1 545 347 6
4 Acura Integra Type R 2001 2001 Acura Integra Type R 00711.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 126 65 735 347 6
5 Acura Integra Type R 2001 2001 Acura Integra Type R 01002.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 54 225 499 610 6
6 Acura Integra Type R 2001 2001 Acura Integra Type R 01035.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 25 23 761 529 6
7 Acura Integra Type R 2001 2001 Acura Integra Type R 01176.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 289 433 1462 904 6
8 Acura Integra Type R 2001 2001 Acura Integra Type R 01326.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 40 108 1250 752 6
9 Acura Integra Type R 2001 2001 Acura Integra Type R 01864.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 78 110 410 314 6
10 Acura Integra Type R 2001 2001 Acura Integra Type R 02011.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 30 27 552 306 6
11 Acura Integra Type R 2001 2001 Acura Integra Type R 02182.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 26 132 1009 675 6
12 Acura Integra Type R 2001 2001 Acura Integra Type R 02425.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 37 172 559 412 6
13 Acura Integra Type R 2001 2001 Acura Integra Type R 02433.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 15 21 632 288 6
14 Acura Integra Type R 2001 2001 Acura Integra Type R 02458.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 21 218 771 511 6
15 Acura Integra Type R 2001 2001 Acura Integra Type R 02595.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 55 149 531 359 6
16 Acura Integra Type R 2001 2001 Acura Integra Type R 02742.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 73 210 439 359 6
17 Acura Integra Type R 2001 2001 Acura Integra Type R 02798.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 123 82 371 246 6
18 Acura Integra Type R 2001 2001 Acura Integra Type R 03056.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 99 270 844 583 6
19 Acura Integra Type R 2001 2001 Acura Integra Type R 03466.jpg C:/Users/SAIF/Desktop/Great Learning/Capstone ... 44 147 595 435 6
In [21]:
Test_final_df.shape
Out[21]:
(8041, 10)
In [ ]:
Lets have a look at the sanity of the data and take appropriate steps if necessary!
In [22]:
Train_final_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8144 entries, 0 to 8143
Data columns (total 10 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   carName         8144 non-null   object
 1   carModel        8144 non-null   object
 2   carModel_1      8144 non-null   object
 3   Image Name      8144 non-null   object
 4   Image Location  8144 non-null   object
 5   xmin            8144 non-null   int64 
 6   ymin            8144 non-null   int64 
 7   xmax            8144 non-null   int64 
 8   ymax            8144 non-null   int64 
 9   Image_class     8144 non-null   int64 
dtypes: int64(5), object(5)
memory usage: 699.9+ KB
In [23]:
Test_final_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8041 entries, 0 to 8040
Data columns (total 10 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   carName         8041 non-null   object
 1   carModel        8041 non-null   object
 2   carModel_1      8041 non-null   object
 3   Image Name      8041 non-null   object
 4   Image Location  8041 non-null   object
 5   xmin            8041 non-null   int64 
 6   ymin            8041 non-null   int64 
 7   xmax            8041 non-null   int64 
 8   ymax            8041 non-null   int64 
 9   Image_class     8041 non-null   int64 
dtypes: int64(5), object(5)
memory usage: 691.0+ KB
In [ ]:
In [24]:
Train_final_df['carModel'].value_counts().index
Out[24]:
Index(['2012', '2007', '2009', '2010', '2011', '2008', '1993', '1994', '1998',
       '2001', '2006', '1991', '2000', '2002', '1997', '1999'],
      dtype='object')
In [25]:
Test_final_df['carModel'].value_counts().index
Out[25]:
Index(['2012', '2007', '2009', '2010', '2011', '2008', '1993', '1994', '1998',
       '2001', '1991', '2002', '2006', '2000', '1999', '1997'],
      dtype='object')
In [ ]:
In [26]:
train_class_percentage = Train_final_df['Image_class'].value_counts(normalize=True) * 100
test_class_percentage = Test_final_df['Image_class'].value_counts(normalize=True) * 100

print("Train Data Class Percentages:")
print(train_class_percentage)

print("\nTest Data Class Percentages:")
print(test_class_percentage)

Train Data Class Percentages:
119    0.834971
79     0.601670
167    0.589391
161    0.589391
144    0.577112
         ...   
175    0.380648
64     0.368369
158    0.356090
99     0.343811
136    0.294695
Name: Image_class, Length: 196, dtype: float64

Test Data Class Percentages:
119    0.845666
161    0.596941
79     0.596941
167    0.584504
43     0.572068
         ...   
175    0.373088
158    0.360652
64     0.360652
99     0.335779
136    0.298470
Name: Image_class, Length: 196, dtype: float64
In [ ]:
Lets explore the data by plotting few visualizations and performing EDA.
1 : Test data VS Train Data
In [ ]:
total_train_rows = Train_final_df.shape[0]
total_test_rows = Test_final_df.shape[0]
train_percentage = (total_train_rows / (total_train_rows + total_test_rows)) * 100
test_percentage = (total_test_rows / (total_train_rows + total_test_rows)) * 100

sizes = [total_train_rows, total_test_rows]
labels = ['Train Data', 'Test Data']
colors = ['lightcoral', 'lightskyblue']
explode = (0.1, 0)

plt.figure(figsize=(8, 6))
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)
plt.title('Train-Test Data Percentage Split')
plt.axis('equal')
plt.show()
No description has been provided for this image
In [ ]:
2 : Cars by year of their origin
In [ ]:
carModel_counts = Train_final_df['carModel'].value_counts().reset_index()
carModel_counts.columns = ['carModel', 'Count']

plt.figure(figsize=(10, 6))
plt.bar(carModel_counts['carModel'], carModel_counts['Count'], color='skyblue')
plt.xlabel('Car Model')
plt.ylabel('Count')
plt.title('Train data Cars by year of origin')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
No description has been provided for this image
In [ ]:
plt.figure(figsize=(10, 6))
plt.bar(carModel_counts['carModel'], carModel_counts['Count'], color='skyblue')
plt.xlabel('Car Model')
plt.ylabel('Count')
plt.title('Test data Cars by year of origin')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
No description has been provided for this image
In [ ]:
3 : Count of top 10
In [ ]:
top_10_carModel_1 = Train_final_df['carModel_1'].value_counts().nlargest(10)
bar_colors = ['skyblue', 'orange', 'green', 'red', 'purple', 'pink', 'yellow', 'cyan', 'magenta', 'lightgreen']
plt.figure(figsize=(10, 6))
plt.bar(top_10_carModel_1.index, top_10_carModel_1.values, color=bar_colors)
plt.xlabel('Car Model_1')
plt.ylabel('Count')
plt.title('Top 10 Train Cars')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
No description has been provided for this image
In [ ]:
top_10_carModel_1 = Test_final_df['carModel_1'].value_counts().nlargest(10)
bar_colors = ['skyblue', 'orange', 'green', 'red', 'purple', 'pink', 'yellow', 'cyan', 'magenta', 'lightgreen']
plt.figure(figsize=(10, 6))
plt.bar(top_10_carModel_1.index, top_10_carModel_1.values, color=bar_colors)
plt.xlabel('Car Model_1')
plt.ylabel('Count')
plt.title('Top 10 Test Cars')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
Step 4 : Display images with bounding box
In [ ]:
def display_image_with_bounding_box(img_num):
    img_path = Train_data_df.loc[img_num, 'Image Location']
    img = cv2.imread(img_path)

    xmin = int(Train_final_df.loc[img_num, 'xmin'])
    ymin = int(Train_final_df.loc[img_num, 'ymin'])
    xmax = int(Train_final_df.loc[img_num, 'xmax'])
    ymax = int(Train_final_df.loc[img_num, 'ymax'])

    cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)

    car_model = Train_data_df.loc[img_num, 'carModel_1']

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    plt.figure(figsize=(8, 6))
    plt.imshow(img)
    plt.title(f'Image with Bounding Box - {Train_data_df.loc[img_num, "Image Name"]}')
    plt.axis('off')  # Remove axis
    plt.show()

    print(f'Coordinates: xmin={xmin}, ymin={ymin}, xmax={xmax}, ymax={ymax}')
    print(f'Car Model: {car_model}')

random_indices = random.sample(range(len(Train_data_df)), 5)
for img_num in random_indices:
    display_image_with_bounding_box(img_num)
No description has been provided for this image 
Coordinates: xmin=108, ymin=37, xmax=682, ymax=555
Car Model: HUMMER H3T Crew Cab
No description has been provided for this image 
Coordinates: xmin=12, ymin=58, xmax=591, ymax=302
Car Model: Nissan 240SX Coupe
No description has been provided for this image 
Coordinates: xmin=7, ymin=164, xmax=636, ymax=342
Car Model: Lincoln Town Car Sedan
No description has been provided for this image 
Coordinates: xmin=53, ymin=215, xmax=723, ymax=517
Car Model: Audi S4 Sedan
No description has been provided for this image 
Coordinates: xmin=126, ymin=98, xmax=822, ymax=572
Car Model: HUMMER H2 SUT Crew Cab
In [ ]:
Step 5 : Design, train and test basic CNN models to classify the car

!pip install tensorflow

In [ ]:
Model 1
In [ ]:
img_width, img_height = 224, 224
batch_size = 16
train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.1,
                                   zoom_range=0.1,
                                   horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)

train_data_dir = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Train Images'
test_data_dir = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Test Images'

batch_size = 32
num_classes = len(Train_final_df['Image_class'].unique())

condition = Train_final_df['Image_class'] == 'car'
class_indices = np.where(condition)[0]
class_labels = Train_final_df['Image_class'].iloc[class_indices].tolist()

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=class_labels)

validation_classes = sorted(os.listdir(test_data_dir))

validation_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes)

model_1 = Sequential()
model_1.add(Conv2D(16, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model_1.add(MaxPooling2D(pool_size=(2, 2)))
model_1.add(Conv2D(32, (3, 3), activation='relu'))
model_1.add(MaxPooling2D(pool_size=(2, 2)))
model_1.add(Conv2D(128, (3, 3), activation='relu'))
model_1.add(MaxPooling2D(pool_size=(2, 2)))
model_1.add(Flatten())
model_1.add(Dense(64, activation='relu'))
model_1.add(Dense(num_classes, activation='softmax'))

model_1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

history_1 = model_1.fit(
    train_generator,
    steps_per_epoch=len(Train_final_df) // batch_size,
    epochs=10,
    validation_data=validation_generator,
    validation_steps=len(Train_final_df) // batch_size)

Found 8144 images belonging to 196 classes.
Found 8041 images belonging to 196 classes.
Epoch 1/10
254/254 [==============================] - ETA: 0s - loss: 5.2587 - accuracy: 0.0062WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 254 batches). You may need to use the repeat() function when building your dataset.
254/254 [==============================] - 291s 1s/step - loss: 5.2587 - accuracy: 0.0062 - val_loss: 5.1854 - val_accuracy: 0.0097
Epoch 2/10
254/254 [==============================] - 203s 800ms/step - loss: 5.1590 - accuracy: 0.0128
Epoch 3/10
254/254 [==============================] - 204s 804ms/step - loss: 5.0984 - accuracy: 0.0144
Epoch 4/10
254/254 [==============================] - 204s 805ms/step - loss: 4.9845 - accuracy: 0.0255
Epoch 5/10
254/254 [==============================] - 209s 824ms/step - loss: 4.8028 - accuracy: 0.0475
Epoch 6/10
254/254 [==============================] - 196s 771ms/step - loss: 4.5783 - accuracy: 0.0690
Epoch 7/10
254/254 [==============================] - 197s 775ms/step - loss: 4.3333 - accuracy: 0.0897
Epoch 8/10
254/254 [==============================] - 196s 770ms/step - loss: 4.0716 - accuracy: 0.1312
Epoch 9/10
254/254 [==============================] - 194s 764ms/step - loss: 3.8233 - accuracy: 0.1658
Epoch 10/10
254/254 [==============================] - 196s 771ms/step - loss: 3.5496 - accuracy: 0.2161
In [ ]:
Lets save the Model and Model Weights
In [ ]:
model_1.save('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1.h5')
model_1.save_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1_weights.h5')
loaded_model_1 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1.h5')
loaded_model_1.load_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_1_weights.h5')
In [ ]:
Lets analyze the model 1 performance on our Test Data
In [ ]:
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes
)
test_loss, test_acc = loaded_model_1.evaluate(test_generator)
print("Test Accuracy:", test_acc)
print("Test Loss:", test_loss)

Found 8041 images belonging to 196 classes.
252/252 [==============================] - 75s 299ms/step - loss: 5.2131 - accuracy: 0.0466
Test Accuracy: 0.04663598909974098
Test Loss: 5.213118076324463
In [ ]:
Let plot the Loss as well as Accuracy for Model 1
In [ ]:
plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_1.history['loss']) + 1), history_1.history['loss'], label='Training Loss', marker='o')
plt.title('Model 1 - Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_1.history['accuracy']) + 1), history_1.history['accuracy'], label='Training Accuracy', marker='o')
plt.title('Model 1 - Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.grid(True)
plt.show()
No description has been provided for this image
No description has been provided for this image
In [ ]:
In [ ]:
Model 2
In [ ]:
model_2 = Sequential()
model_2.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(Conv2D(64, (3, 3), activation='sigmoid'))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
# Number of Conventional Layers reduced

model_2.add(Flatten())
model_2.add(Dense(64, activation='sigmoid'))
model_2.add(Dense(num_classes, activation='sigmoid'))  # Activation Function for output layer changed

model_2.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
# Optimizer changed from adam to Standard-Gradient-Descent(SGD)

history_2 = model_2.fit(
    train_generator,
    steps_per_epoch=len(Train_final_df) // batch_size,
    epochs=5,  # Reduced epochs for faster training of basic model
    validation_data=validation_generator,
    validation_steps=len(Train_final_df) // batch_size)

Epoch 1/5
254/254 [==============================] - ETA: 0s - loss: 5.2871 - accuracy: 0.0053WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 254 batches). You may need to use the repeat() function when building your dataset.
254/254 [==============================] - 393s 2s/step - loss: 5.2871 - accuracy: 0.0053 - val_loss: 5.2779 - val_accuracy: 0.0055
Epoch 2/5
254/254 [==============================] - 275s 1s/step - loss: 5.2787 - accuracy: 0.0063
Epoch 3/5
254/254 [==============================] - 274s 1s/step - loss: 5.2787 - accuracy: 0.0065
Epoch 4/5
254/254 [==============================] - 277s 1s/step - loss: 5.2779 - accuracy: 0.0083
Epoch 5/5
254/254 [==============================] - 274s 1s/step - loss: 5.2779 - accuracy: 0.0083
In [ ]:
Lets save the Model and Model Weights
In [ ]:
model_2.save('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2.h5')
model_2.save_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2_weights.h5')
loaded_model_2 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2.h5')
loaded_model_2.load_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_2_weights.h5')
In [ ]:
Lets analyze the model 2 performance on our Test Data
In [ ]:
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes
)
test_loss, test_acc = loaded_model_2.evaluate(test_generator)
print("Test Accuracy:", test_acc)
print("Test Loss:", test_loss)

Found 8041 images belonging to 196 classes.
252/252 [==============================] - 95s 375ms/step - loss: 5.2773 - accuracy: 0.0085
Test Accuracy: 0.008456659503281116
Test Loss: 5.277335166931152
In [ ]:
Let plot the Loss as well as Accuracy for Model 2
In [ ]:
plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_2.history['loss']) + 1), history_2.history['loss'], label='Training Loss', marker='o')
plt.title('Model 1 - Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_2.history['accuracy']) + 1), history_2.history['accuracy'], label='Training Accuracy', marker='o')
plt.title('Model 1 - Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.grid(True)
plt.show()
No description has been provided for this image
No description has been provided for this image
In [ ]:
In [ ]:
Model 3
In [ ]:
model_3 = Sequential()
model_3.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(64, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(128, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(256, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Conv2D(512, (3, 3), activation='relu'))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(Flatten())
model_3.add(Dense(512, activation='relu'))
model_3.add(Dropout(0.5))
model_3.add(Dense(num_classes, activation='softmax'))

model_3.compile(loss='categorical_crossentropy', optimizer='adagrad', metrics=['accuracy'])

early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)

history_3 = model_3.fit(
    train_generator,
    steps_per_epoch=len(train_generator),
    epochs=10,
    validation_data=validation_generator,
    validation_steps=len(validation_generator),
    callbacks=[early_stopping])

Epoch 1/10
255/255 [==============================] - 435s 2s/step - loss: 5.2806 - accuracy: 0.0047 - val_loss: 5.2779 - val_accuracy: 0.0057
Epoch 2/10
255/255 [==============================] - 433s 2s/step - loss: 5.2789 - accuracy: 0.0048 - val_loss: 5.2768 - val_accuracy: 0.0073
Epoch 3/10
255/255 [==============================] - 434s 2s/step - loss: 5.2772 - accuracy: 0.0070 - val_loss: 5.2763 - val_accuracy: 0.0056
Epoch 4/10
255/255 [==============================] - 430s 2s/step - loss: 5.2775 - accuracy: 0.0048 - val_loss: 5.2759 - val_accuracy: 0.0065
Epoch 5/10
255/255 [==============================] - 434s 2s/step - loss: 5.2766 - accuracy: 0.0056 - val_loss: 5.2756 - val_accuracy: 0.0075
Epoch 6/10
255/255 [==============================] - 432s 2s/step - loss: 5.2767 - accuracy: 0.0070 - val_loss: 5.2753 - val_accuracy: 0.0073
Epoch 7/10
255/255 [==============================] - 432s 2s/step - loss: 5.2757 - accuracy: 0.0061 - val_loss: 5.2751 - val_accuracy: 0.0075
Epoch 8/10
255/255 [==============================] - 430s 2s/step - loss: 5.2754 - accuracy: 0.0063 - val_loss: 5.2747 - val_accuracy: 0.0078
Epoch 9/10
255/255 [==============================] - 433s 2s/step - loss: 5.2761 - accuracy: 0.0076 - val_loss: 5.2745 - val_accuracy: 0.0085
Epoch 10/10
255/255 [==============================] - 432s 2s/step - loss: 5.2752 - accuracy: 0.0060 - val_loss: 5.2742 - val_accuracy: 0.0085
In [ ]:
Lets save the Model and Model Weights
In [ ]:
model_3.save('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3.h5')
model_3.save_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3_weights.h5')
loaded_model_3 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3.h5')
loaded_model_3.load_weights('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 1/Milestone_1_Model_3_weights.h5')
In [ ]:
Lets analyze the model 3 performance on our Test Data
In [ ]:
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes
)
test_loss, test_acc = loaded_model_3.evaluate(test_generator)
print("Test Accuracy:", test_acc)
print("Test Loss:", test_loss)

Found 8041 images belonging to 196 classes.
252/252 [==============================] - 116s 458ms/step - loss: 5.2742 - accuracy: 0.0085
Test Accuracy: 0.008456659503281116
Test Loss: 5.274209499359131
In [ ]:
Let plot the Training and Validation Loss as well as Training and Validation Accuracy for Model 3
In [ ]:
plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_3.history['loss']) + 1), history_3.history['loss'], label='Training Loss', marker='o')
plt.title('Model 1 - Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(history_3.history['accuracy']) + 1), history_3.history['accuracy'], label='Training Accuracy', marker='o')
plt.title('Model 1 - Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.grid(True)
plt.show()
No description has been provided for this image
No description has been provided for this image
In [ ]:
Lets compare and evaluate the performace of all the 3 CNN models
In [ ]:
train_acc_1 = history_1.history['accuracy']
test_acc_1 = history_1.history['val_accuracy']
train_loss_1 = history_1.history['loss']
test_loss_1 = history_1.history['val_loss']

train_acc_2 = history_2.history['accuracy']
test_acc_2 = history_2.history['val_accuracy']
train_loss_2 = history_2.history['loss']
test_loss_2 = history_2.history['val_loss']

train_acc_3 = history_3.history['accuracy']
test_acc_3 = history_3.history['val_accuracy']
train_loss_3 = history_3.history['loss']
test_loss_3 = history_3.history['val_loss']

final_train_acc_1 = train_acc_1[-1]
final_test_acc_1 = test_acc_1[-1]
final_train_loss_1 = train_loss_1[-1]
final_test_loss_1 = test_loss_1[-1]

final_train_acc_2 = train_acc_2[-1]
final_test_acc_2 = test_acc_2[-1]
final_train_loss_2 = train_loss_2[-1]
final_test_loss_2 = test_loss_2[-1]

final_train_acc_3 = train_acc_3[-1]
final_test_acc_3 = test_acc_3[-1]
final_train_loss_3 = train_loss_3[-1]
final_test_loss_3 = test_loss_3[-1]

metrics_data = {
    'Model': ['Model 1', 'Model 2', 'Model 3'],
    'Train Accuracy': [final_train_acc_1, final_train_acc_2, final_train_acc_3],
    'Test Accuracy': [final_test_acc_1, final_test_acc_2, final_test_acc_3],
    'Train Loss': [final_train_loss_1, final_train_loss_2, final_train_loss_3],
    'Test Loss': [final_test_loss_1, final_test_loss_2, final_test_loss_3]
}

metrics_df = pd.DataFrame(metrics_data)
metrics_df
Out[ ]:
Model Train Accuracy Test Accuracy Train Loss Test Loss
0 Model 1 0.216100 0.009700 3.549627 5.185400
1 Model 2 0.008259 0.005472 5.277876 5.277879
2 Model 3 0.006017 0.008457 5.275213 5.274206
In [ ]:

Insights and Observations :

Model 1:
  • Train Accuracy: 21.61%
  • Test Accuracy: 0.97%
  • Train Loss: 3.55
  • Test Loss: 5.19
  • This model demonstrates a notably higher train accuracy compared to the test accuracy, indicating potential overfitting. The high discrepancy between the train and test accuracies suggests that the model may not generalize well to unseen data.
Model 2:
  • Train Accuracy: 0.83%
  • Test Accuracy: 0.55%
  • Train Loss: 5.28
  • Test Loss: 5.28
  • Both the train and test accuracies are quite low, suggesting that this model is not capturing the patterns in the data effectively. The similar train and test losses indicate that the model is not overfitting, but it is not learning the underlying patterns well.
Model 3:
  • Train Accuracy: 0.60%
  • Test Accuracy: 0.85%
  • Train Loss: 5.28
  • Test Loss: 5.27
  • Similar to Model 2, this model also shows low train and test accuracies. However, the test accuracy is comparatively higher than the train accuracy, indicating that it might be performing slightly better on unseen data.
Overall Assessment:
  • All three models exhibit low test accuracies, suggesting that they are not effectively capturing the patterns in the data.
  • Model 1 shows signs of overfitting, while Models 2 and 3 display poor performance on both the train and test sets.
Methods of Optimization:
  • The activation function (sigmoid) and optimizer (SGD) selected by Model 2 for the output layer are different from those of the other models (softmax, Adam and Adagrad).
  • The variations in model performance that have been reported may be attributed to these changes in optimization strategies.
In [ ]:
Let's now, at last, see how well each of our three models has done in terms of classifying the car photos.
Model 1
In [ ]:
num_images_to_test = 5
current_index = 0

while current_index < len(test_images):
    actual_label = test_labels[current_index].argmax()
    predictions = loaded_model_1.predict(test_images[current_index].reshape(1, img_width, img_height, 3))
    predicted_label = predictions[0].argmax()
    plt.imshow(test_images[current_index])
    plt.title(f'Actual: {actual_label}, Predicted: {predicted_label}')
    plt.show()
    choice = input("Press 'n' to see the next image, or 'q' to quit: ")
    if choice == 'n':
        current_index += 1
    elif choice == 'q':
        break
    else:
        print("Invalid choice. Please try again.")

print("End of interactive image display.")

1/1 [==============================] - 0s 86ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 56ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 35ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 36ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 35ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 35ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: q
End of interactive image display.
Model 2
In [ ]:
num_images_to_test = 5
current_index = 0

while current_index < len(test_images):
    actual_label = test_labels[current_index].argmax()
    predictions = loaded_model_2.predict(test_images[current_index].reshape(1, img_width, img_height, 3))
    predicted_label = predictions[0].argmax()
    plt.imshow(test_images[current_index])
    plt.title(f'Actual: {actual_label}, Predicted: {predicted_label}')
    plt.show()
    choice = input("Press 'n' to see the next image, or 'q' to quit: ")
    if choice == 'n':
        current_index += 1
    elif choice == 'q':
        break
    else:
        print("Invalid choice. Please try again.")

print("End of interactive image display.")

1/1 [==============================] - 0s 40ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 45ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 42ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 44ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 46ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 36ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: q
End of interactive image display.
Model 3
In [ ]:
num_images_to_test = 5
current_index = 0

while current_index < len(test_images):
    actual_label = test_labels[current_index].argmax()
    predictions = loaded_model_3.predict(test_images[current_index].reshape(1, img_width, img_height, 3))
    predicted_label = predictions[0].argmax()
    plt.imshow(test_images[current_index])
    plt.title(f'Actual: {actual_label}, Predicted: {predicted_label}')
    plt.show()
    choice = input("Press 'n' to see the next image, or 'q' to quit: ")
    if choice == 'n':
        current_index += 1
    elif choice == 'q':
        break
    else:
        print("Invalid choice. Please try again.")

print("End of interactive image display.")

1/1 [==============================] - 0s 27ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 38ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 27ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 27ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 28ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: n
1/1 [==============================] - 0s 29ms/step
No description has been provided for this image 
Press 'n' to see the next image, or 'q' to quit: q
End of interactive image display.
In [ ]:

Milestone 2

Step 1: Fine tune the trained basic CNN models to classify the car.
In [ ]:

Lets create a copy of our original Train Dataset Train_final_df

Model 1 : With Transfer Learning Model : ResNet_50

In [63]:
# Creating a copy of the DataFrame
Train_final_df_copy = Train_final_df.copy()

# Preprocessing class labels using label encoder
label_encoder = LabelEncoder()
Train_final_df_copy['Encoded_Labels'] = label_encoder.fit_transform(Train_final_df['Image_class'])
num_classes = len(label_encoder.classes_)

# Splitting data into training and validation sets
train_data_new, val_data = train_test_split(Train_final_df_copy, test_size=0.2, random_state=42)

# Defining image parameters
IMAGE_SIZE = (224, 224)
BATCH_SIZE = 64

# Performing data augmentation for training images
train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

# Preprocessing images for validation and test
val_test_datagen = ImageDataGenerator(rescale=1./255)

# Converting integer labels back to class names and converting them to strings
train_data_new['Class_Labels'] = label_encoder.inverse_transform(train_data_new['Encoded_Labels']).astype(str)
val_data['Class_Labels'] = label_encoder.inverse_transform(val_data['Encoded_Labels']).astype(str)

# Defining data generators for training and validation
train_generator = train_datagen.flow_from_dataframe(dataframe=train_data_new,
                                                    x_col="Image Location",
                                                    y_col="Class_Labels",  # Using class names as y_col
                                                    target_size=IMAGE_SIZE,
                                                    batch_size=BATCH_SIZE,
                                                    class_mode='categorical',  # Setting class_mode to 'categorical'
                                                    shuffle=True)  # Shuffling the data

val_generator = val_test_datagen.flow_from_dataframe(dataframe=val_data,
                                                      x_col="Image Location",
                                                      y_col="Class_Labels",  # Using class names as y_col
                                                      target_size=IMAGE_SIZE,
                                                      batch_size=BATCH_SIZE,
                                                      class_mode='categorical',  # Setting class_mode to 'categorical'
                                                      shuffle=False)  # Not shuffling validation data

# Loading pre-trained ResNet50 model
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)  # Adding an additional dense layer
x = Dense(256, activation='relu')(x)  # Adding another dense layer
predictions = Dense(num_classes, activation='softmax')(x)  # Output layer, adjusted to num_classes

# Creating the transfer learning model
model_reset50 = Model(inputs=base_model.input, outputs=predictions)

# Freezing the first 50 layers of the pre-trained model
for layer in model_reset50.layers[:51]:
    layer.trainable = False

# Training the remaining layers
for layer in model_reset50.layers[51:]:
    layer.trainable = True

# Compiling the model
model_reset50.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])

# Printing model summary
model_reset50.summary()

# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('best_model.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)

Found 6515 validated image filenames belonging to 196 classes.
Found 1629 validated image filenames belonging to 196 classes.
Model: "model_4"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_9 (InputLayer)        [(None, 224, 224, 3)]        0         []                            
                                                                                                  
 conv1_pad (ZeroPadding2D)   (None, 230, 230, 3)          0         ['input_9[0][0]']             
                                                                                                  
 conv1_conv (Conv2D)         (None, 112, 112, 64)         9472      ['conv1_pad[0][0]']           
                                                                                                  
 conv1_bn (BatchNormalizati  (None, 112, 112, 64)         256       ['conv1_conv[0][0]']          
 on)                                                                                              
                                                                                                  
 conv1_relu (Activation)     (None, 112, 112, 64)         0         ['conv1_bn[0][0]']            
                                                                                                  
 pool1_pad (ZeroPadding2D)   (None, 114, 114, 64)         0         ['conv1_relu[0][0]']          
                                                                                                  
 pool1_pool (MaxPooling2D)   (None, 56, 56, 64)           0         ['pool1_pad[0][0]']           
                                                                                                  
 conv2_block1_1_conv (Conv2  (None, 56, 56, 64)           4160      ['pool1_pool[0][0]']          
 D)                                                                                               
                                                                                                  
 conv2_block1_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block1_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv2_block1_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block1_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv2_block1_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block1_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv2_block1_0_conv (Conv2  (None, 56, 56, 256)          16640     ['pool1_pool[0][0]']          
 D)                                                                                               
                                                                                                  
 conv2_block1_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block1_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv2_block1_0_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_0_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block1_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block1_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_0_bn[0][0]',   
                                                                     'conv2_block1_3_bn[0][0]']   
                                                                                                  
 conv2_block1_out (Activati  (None, 56, 56, 256)          0         ['conv2_block1_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv2_block2_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block1_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv2_block2_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block2_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv2_block2_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block2_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv2_block2_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block2_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv2_block2_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block2_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv2_block2_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block2_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block2_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_out[0][0]',    
                                                                     'conv2_block2_3_bn[0][0]']   
                                                                                                  
 conv2_block2_out (Activati  (None, 56, 56, 256)          0         ['conv2_block2_add[0][0]']    

 on)                                                                                              
                                                                                                  
 conv2_block3_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block2_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv2_block3_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block3_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv2_block3_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block3_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv2_block3_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block3_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv2_block3_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block3_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv2_block3_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block3_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv2_block3_add (Add)      (None, 56, 56, 256)          0         ['conv2_block2_out[0][0]',    
                                                                     'conv2_block3_3_bn[0][0]']   
                                                                                                  
 conv2_block3_out (Activati  (None, 56, 56, 256)          0         ['conv2_block3_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv3_block1_1_conv (Conv2  (None, 28, 28, 128)          32896     ['conv2_block3_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv3_block1_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block1_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block1_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block1_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block1_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block1_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block1_0_conv (Conv2  (None, 28, 28, 512)          131584    ['conv2_block3_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv3_block1_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block1_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block1_0_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_0_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block1_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block1_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_0_bn[0][0]',   
                                                                     'conv3_block1_3_bn[0][0]']   
                                                                                                  
 conv3_block1_out (Activati  (None, 28, 28, 512)          0         ['conv3_block1_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv3_block2_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block1_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv3_block2_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block2_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block2_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block2_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block2_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  

 conv3_block2_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block2_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block2_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block2_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block2_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block2_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_out[0][0]',    
                                                                     'conv3_block2_3_bn[0][0]']   
                                                                                                  
 conv3_block2_out (Activati  (None, 28, 28, 512)          0         ['conv3_block2_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv3_block3_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block2_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv3_block3_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block3_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block3_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block3_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block3_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block3_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block3_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block3_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block3_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block3_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block3_add (Add)      (None, 28, 28, 512)          0         ['conv3_block2_out[0][0]',    
                                                                     'conv3_block3_3_bn[0][0]']   
                                                                                                  
 conv3_block3_out (Activati  (None, 28, 28, 512)          0         ['conv3_block3_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv3_block4_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block3_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv3_block4_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block4_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block4_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block4_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block4_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block4_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv3_block4_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block4_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv3_block4_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block4_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv3_block4_add (Add)      (None, 28, 28, 512)          0         ['conv3_block3_out[0][0]',    
                                                                     'conv3_block4_3_bn[0][0]']   
                                                                                                  
 conv3_block4_out (Activati  (None, 28, 28, 512)          0         ['conv3_block4_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv4_block1_1_conv (Conv2  (None, 14, 14, 256)          131328    ['conv3_block4_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv4_block1_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block1_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_1_bn[0][0]']   
 ation)                                                                                           

                                                                                                  
 conv4_block1_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block1_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block1_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block1_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block1_0_conv (Conv2  (None, 14, 14, 1024)         525312    ['conv3_block4_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv4_block1_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block1_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block1_0_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_0_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block1_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block1_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_0_bn[0][0]',   
                                                                     'conv4_block1_3_bn[0][0]']   
                                                                                                  
 conv4_block1_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block1_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv4_block2_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block1_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv4_block2_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block2_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block2_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block2_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block2_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block2_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block2_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block2_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block2_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block2_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block2_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_out[0][0]',    
                                                                     'conv4_block2_3_bn[0][0]']   
                                                                                                  
 conv4_block2_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block2_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv4_block3_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block2_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv4_block3_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block3_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block3_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block3_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block3_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block3_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block3_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block3_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block3_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block3_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block3_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block2_out[0][0]',    

                                                                     'conv4_block3_3_bn[0][0]']   
                                                                                                  
 conv4_block3_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block3_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv4_block4_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block3_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv4_block4_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block4_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block4_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block4_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block4_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block4_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block4_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block4_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block4_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block4_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block4_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block3_out[0][0]',    
                                                                     'conv4_block4_3_bn[0][0]']   
                                                                                                  
 conv4_block4_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block4_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv4_block5_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block4_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv4_block5_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block5_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block5_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block5_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block5_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block5_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block5_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block5_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block5_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block5_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block5_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block4_out[0][0]',    
                                                                     'conv4_block5_3_bn[0][0]']   
                                                                                                  
 conv4_block5_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block5_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv4_block6_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block5_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv4_block6_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block6_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv4_block6_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block6_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block6_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block6_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  

 conv4_block6_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block6_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv4_block6_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block6_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv4_block6_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block5_out[0][0]',    
                                                                     'conv4_block6_3_bn[0][0]']   
                                                                                                  
 conv4_block6_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block6_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv5_block1_1_conv (Conv2  (None, 7, 7, 512)            524800    ['conv4_block6_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv5_block1_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block1_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv5_block1_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block1_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv5_block1_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block1_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv5_block1_0_conv (Conv2  (None, 7, 7, 2048)           2099200   ['conv4_block6_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv5_block1_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv5_block1_0_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_0_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block1_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block1_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_0_bn[0][0]',   
                                                                     'conv5_block1_3_bn[0][0]']   
                                                                                                  
 conv5_block1_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block1_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv5_block2_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block1_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv5_block2_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_1_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block2_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv5_block2_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block2_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv5_block2_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block2_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv5_block2_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv5_block2_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block2_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block2_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_out[0][0]',    
                                                                     'conv5_block2_3_bn[0][0]']   
                                                                                                  
 conv5_block2_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block2_add[0][0]']    
 on)                                                                                              
                                                                                                  
 conv5_block3_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block2_out[0][0]']    
 D)                                                                                               
                                                                                                  
 conv5_block3_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_1_conv[0][0]'] 
 rmalization)                                                                                     

                                                                                                  
 conv5_block3_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_1_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv5_block3_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block3_1_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv5_block3_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_2_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block3_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_2_bn[0][0]']   
 ation)                                                                                           
                                                                                                  
 conv5_block3_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] 
 D)                                                                                               
                                                                                                  
 conv5_block3_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block3_3_conv[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv5_block3_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block2_out[0][0]',    
                                                                     'conv5_block3_3_bn[0][0]']   
                                                                                                  
 conv5_block3_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block3_add[0][0]']    
 on)                                                                                              
                                                                                                  
 global_average_pooling2d_4  (None, 2048)                 0         ['conv5_block3_out[0][0]']    
  (GlobalAveragePooling2D)                                                                        
                                                                                                  
 dense_16 (Dense)            (None, 1024)                 2098176   ['global_average_pooling2d_4[0
                                                                    ][0]']                        
                                                                                                  
 dense_17 (Dense)            (None, 512)                  524800    ['dense_16[0][0]']            
                                                                                                  
 dense_18 (Dense)            (None, 256)                  131328    ['dense_17[0][0]']            
                                                                                                  
 dense_19 (Dense)            (None, 196)                  50372     ['dense_18[0][0]']            
                                                                                                  
==================================================================================================
Total params: 26392388 (100.68 MB)
Trainable params: 25731780 (98.16 MB)
Non-trainable params: 660608 (2.52 MB)
__________________________________________________________________________________________________
In [65]:
# Training the model
history_model_reset50 = model_reset50.fit(train_generator,
                            steps_per_epoch=len(train_generator),
                            epochs=5,
                            validation_data=val_generator,
                            validation_steps=len(val_generator),
                            callbacks=[checkpoint])

Epoch 1/5
102/102 [==============================] - ETA: 0s - loss: 5.2918 - accuracy: 0.0051
Epoch 1: val_accuracy improved from -inf to 0.00368, saving model to best_model.h5
102/102 [==============================] - 923s 9s/step - loss: 5.2918 - accuracy: 0.0051 - val_loss: 5.2940 - val_accuracy: 0.0037
Epoch 2/5
102/102 [==============================] - ETA: 0s - loss: 5.2781 - accuracy: 0.0072
Epoch 2: val_accuracy improved from 0.00368 to 0.00614, saving model to best_model.h5
102/102 [==============================] - 891s 9s/step - loss: 5.2781 - accuracy: 0.0072 - val_loss: 5.3092 - val_accuracy: 0.0061
Epoch 3/5
102/102 [==============================] - ETA: 0s - loss: 5.2775 - accuracy: 0.0080
Epoch 3: val_accuracy improved from 0.00614 to 0.00737, saving model to best_model.h5
102/102 [==============================] - 918s 9s/step - loss: 5.2775 - accuracy: 0.0080 - val_loss: 5.3199 - val_accuracy: 0.0074
Epoch 4/5
102/102 [==============================] - ETA: 0s - loss: 5.2758 - accuracy: 0.0086
Epoch 4: val_accuracy did not improve from 0.00737
102/102 [==============================] - 895s 9s/step - loss: 5.2758 - accuracy: 0.0086 - val_loss: 5.4418 - val_accuracy: 0.0074
Epoch 5/5
102/102 [==============================] - ETA: 0s - loss: 5.2758 - accuracy: 0.0081
Epoch 5: val_accuracy did not improve from 0.00737
102/102 [==============================] - 892s 9s/step - loss: 5.2758 - accuracy: 0.0081 - val_loss: 5.3185 - val_accuracy: 0.0074
In [28]:
loaded_model_1= load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_resnet50.h5')

WARNING:tensorflow:From C:\Users\SAIF\anaconda3\Lib\site-packages\keras\src\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.

WARNING:tensorflow:From C:\Users\SAIF\anaconda3\Lib\site-packages\keras\src\layers\pooling\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
In [29]:
evaluation = loaded_model_1.evaluate(val_generator)
print("Validation Accuracy:", evaluation[1])

WARNING:tensorflow:From C:\Users\SAIF\anaconda3\Lib\site-packages\keras\src\utils\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.

26/26 [==============================] - 99s 4s/step - loss: 5.3199 - accuracy: 0.0074
Validation Accuracy: 0.007366482634097338
In [ ]:

Model 2 ; Inception Model Containing the following models : {vgg19 + resnet50 + mobilenet + efficientnet}

In [57]:
In [34]:
# Instantiating pre-trained models (VGG19, ResNet50, MobileNet, EfficientNetB0)
vgg19_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
resnet50_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
mobilenet_model = MobileNet(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
efficientnet_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Freezing layers in each pre-trained model
for model in [vgg19_model, resnet50_model, mobilenet_model, efficientnet_model]:
    for layer in model.layers:
        layer.trainable = False

# Defining input layer
input_layer = tf.keras.Input(shape=(224, 224, 3))

# Getting outputs from each pre-trained model
vgg19_output = vgg19_model(input_layer)
resnet50_output = resnet50_model(input_layer)
mobilenet_output = mobilenet_model(input_layer)
efficientnet_output = efficientnet_model(input_layer)

# Concatenating outputs
concatenated_output = Concatenate()([vgg19_output, resnet50_output, mobilenet_output, efficientnet_output])

# Global average pooling and dense layers
x = GlobalAveragePooling2D()(concatenated_output)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
x = Dense(256, activation='relu')(x)
predictions = Dense(196, activation='softmax')(x)  

# Creating the ensemble model
ensemble_model_1 = Model(inputs=input_layer, outputs=predictions)
ensemble_model_1.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
ensemble_model_1.summary()

Model: "model_1"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_10 (InputLayer)       [(None, 224, 224, 3)]        0         []                            
                                                                                                  
 vgg19 (Functional)          (None, 7, 7, 512)            2002438   ['input_10[0][0]']            
                                                          4                                       
                                                                                                  
 resnet50 (Functional)       (None, 7, 7, 2048)           2358771   ['input_10[0][0]']            
                                                          2                                       
                                                                                                  
 mobilenet_1.00_224 (Functi  (None, 7, 7, 1024)           3228864   ['input_10[0][0]']            
 onal)                                                                                            
                                                                                                  
 efficientnetb0 (Functional  (None, 7, 7, 1280)           4049571   ['input_10[0][0]']            
 )                                                                                                
                                                                                                  
 concatenate_1 (Concatenate  (None, 7, 7, 4864)           0         ['vgg19[0][0]',               
 )                                                                   'resnet50[0][0]',            
                                                                     'mobilenet_1.00_224[0][0]',  
                                                                     'efficientnetb0[0][0]']      
                                                                                                  
 global_average_pooling2d_1  (None, 4864)                 0         ['concatenate_1[0][0]']       
  (GlobalAveragePooling2D)                                                                        
                                                                                                  
 dense_4 (Dense)             (None, 1024)                 4981760   ['global_average_pooling2d_1[0
                                                                    ][0]']                        
                                                                                                  
 dense_5 (Dense)             (None, 512)                  524800    ['dense_4[0][0]']             
                                                                                                  
 dense_6 (Dense)             (None, 256)                  131328    ['dense_5[0][0]']             
                                                                                                  
 dense_7 (Dense)             (None, 196)                  50372     ['dense_6[0][0]']             
                                                                                                  
==================================================================================================
Total params: 56578791 (215.83 MB)
Trainable params: 5688260 (21.70 MB)
Non-trainable params: 50890531 (194.13 MB)
__________________________________________________________________________________________________
In [35]:
# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('best_model_2_inception.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)

early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
history_ensemble_model_1 = ensemble_model_1.fit(
    train_generator,
    steps_per_epoch=len(train_generator),
    epochs=5,
    validation_data=val_generator,
    validation_steps=len(val_generator),
    callbacks=[checkpoint, early_stopping] 
)

Epoch 1/5
102/102 [==============================] - ETA: 0s - loss: 5.3009 - accuracy: 0.0060 
Epoch 1: val_accuracy improved from -inf to 0.00859, saving model to best_model_2_inception.h5
102/102 [==============================] - 1456s 14s/step - loss: 5.3009 - accuracy: 0.0060 - val_loss: 5.2379 - val_accuracy: 0.0086
Epoch 2/5
102/102 [==============================] - ETA: 0s - loss: 4.8528 - accuracy: 0.0215 
Epoch 2: val_accuracy improved from 0.00859 to 0.01903, saving model to best_model_2_inception.h5
102/102 [==============================] - 1433s 14s/step - loss: 4.8528 - accuracy: 0.0215 - val_loss: 4.6259 - val_accuracy: 0.0190
Epoch 3/5
102/102 [==============================] - ETA: 0s - loss: 4.2237 - accuracy: 0.0462 
Epoch 3: val_accuracy improved from 0.01903 to 0.05341, saving model to best_model_2_inception.h5
102/102 [==============================] - 1477s 14s/step - loss: 4.2237 - accuracy: 0.0462 - val_loss: 4.0304 - val_accuracy: 0.0534
Epoch 4/5
102/102 [==============================] - ETA: 0s - loss: 3.8192 - accuracy: 0.0876 
Epoch 4: val_accuracy improved from 0.05341 to 0.09269, saving model to best_model_2_inception.h5
102/102 [==============================] - 1479s 15s/step - loss: 3.8192 - accuracy: 0.0876 - val_loss: 3.8546 - val_accuracy: 0.0927
Epoch 5/5
102/102 [==============================] - ETA: 0s - loss: 3.4608 - accuracy: 0.1358 
Epoch 5: val_accuracy improved from 0.09269 to 0.11602, saving model to best_model_2_inception.h5
102/102 [==============================] - 1450s 14s/step - loss: 3.4608 - accuracy: 0.1358 - val_loss: 3.5215 - val_accuracy: 0.1160
In [30]:
loaded_model_2 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_2_inception.h5')
In [31]:
evaluation = loaded_model_2.evaluate(val_generator)
print("Validation Accuracy:", evaluation[1])

26/26 [==============================] - 286s 11s/step - loss: 3.5215 - accuracy: 0.1160
Validation Accuracy: 0.11602210253477097
In [ ]:

Model 3 : Mobile Net

In [42]:
# Loading pre-trained MobileNetV2 model
base_model_mobilenetv2 = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Adding layers on top of the base model
x = base_model_mobilenetv2.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)  # Additional dense layer
x = Dense(256, activation='relu')(x)  # Additional dense layer
predictions = Dense(196, activation='softmax')(x)  # Output layer, adjust units for your dataset

# Creating the transfer learning model using MobileNetV2 as the base
model_mobilenetv2 = Model(inputs=base_model_mobilenetv2.input, outputs=predictions)

# Freezing the first few layers of the pre-trained MobileNetV2 model
for layer in model_mobilenetv2.layers[:20]:
    layer.trainable = False

# Training the remaining layers
for layer in model_mobilenetv2.layers[20:]:
    layer.trainable = True

model_mobilenetv2.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
model_mobilenetv2.summary()

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5
9406464/9406464 [==============================] - 1s 0us/step
Model: "model_2"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_11 (InputLayer)       [(None, 224, 224, 3)]        0         []                            
                                                                                                  
 Conv1 (Conv2D)              (None, 112, 112, 32)         864       ['input_11[0][0]']            
                                                                                                  
 bn_Conv1 (BatchNormalizati  (None, 112, 112, 32)         128       ['Conv1[0][0]']               
 on)                                                                                              
                                                                                                  
 Conv1_relu (ReLU)           (None, 112, 112, 32)         0         ['bn_Conv1[0][0]']            
                                                                                                  
 expanded_conv_depthwise (D  (None, 112, 112, 32)         288       ['Conv1_relu[0][0]']          
 epthwiseConv2D)                                                                                  
                                                                                                  
 expanded_conv_depthwise_BN  (None, 112, 112, 32)         128       ['expanded_conv_depthwise[0][0
  (BatchNormalization)                                              ]']                           
                                                                                                  
 expanded_conv_depthwise_re  (None, 112, 112, 32)         0         ['expanded_conv_depthwise_BN[0
 lu (ReLU)                                                          ][0]']                        
                                                                                                  
 expanded_conv_project (Con  (None, 112, 112, 16)         512       ['expanded_conv_depthwise_relu
 v2D)                                                               [0][0]']                      
                                                                                                  
 expanded_conv_project_BN (  (None, 112, 112, 16)         64        ['expanded_conv_project[0][0]'
 BatchNormalization)                                                ]                             
                                                                                                  
 block_1_expand (Conv2D)     (None, 112, 112, 96)         1536      ['expanded_conv_project_BN[0][
                                                                    0]']                          
                                                                                                  
 block_1_expand_BN (BatchNo  (None, 112, 112, 96)         384       ['block_1_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_1_expand_relu (ReLU)  (None, 112, 112, 96)         0         ['block_1_expand_BN[0][0]']   
                                                                                                  
 block_1_pad (ZeroPadding2D  (None, 113, 113, 96)         0         ['block_1_expand_relu[0][0]'] 
 )                                                                                                
                                                                                                  
 block_1_depthwise (Depthwi  (None, 56, 56, 96)           864       ['block_1_pad[0][0]']         
 seConv2D)                                                                                        
                                                                                                  
 block_1_depthwise_BN (Batc  (None, 56, 56, 96)           384       ['block_1_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_1_depthwise_relu (Re  (None, 56, 56, 96)           0         ['block_1_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_1_project (Conv2D)    (None, 56, 56, 24)           2304      ['block_1_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_1_project_BN (BatchN  (None, 56, 56, 24)           96        ['block_1_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_2_expand (Conv2D)     (None, 56, 56, 144)          3456      ['block_1_project_BN[0][0]']  
                                                                                                  
 block_2_expand_BN (BatchNo  (None, 56, 56, 144)          576       ['block_2_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_2_expand_relu (ReLU)  (None, 56, 56, 144)          0         ['block_2_expand_BN[0][0]']   
                                                                                                  
 block_2_depthwise (Depthwi  (None, 56, 56, 144)          1296      ['block_2_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_2_depthwise_BN (Batc  (None, 56, 56, 144)          576       ['block_2_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_2_depthwise_relu (Re  (None, 56, 56, 144)          0         ['block_2_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_2_project (Conv2D)    (None, 56, 56, 24)           3456      ['block_2_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_2_project_BN (BatchN  (None, 56, 56, 24)           96        ['block_2_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_2_add (Add)           (None, 56, 56, 24)           0         ['block_1_project_BN[0][0]',  
                                                                     'block_2_project_BN[0][0]']  
                                                                                                  
 block_3_expand (Conv2D)     (None, 56, 56, 144)          3456      ['block_2_add[0][0]']         
                                                                                                  
 block_3_expand_BN (BatchNo  (None, 56, 56, 144)          576       ['block_3_expand[0][0]']      

 rmalization)                                                                                     
                                                                                                  
 block_3_expand_relu (ReLU)  (None, 56, 56, 144)          0         ['block_3_expand_BN[0][0]']   
                                                                                                  
 block_3_pad (ZeroPadding2D  (None, 57, 57, 144)          0         ['block_3_expand_relu[0][0]'] 
 )                                                                                                
                                                                                                  
 block_3_depthwise (Depthwi  (None, 28, 28, 144)          1296      ['block_3_pad[0][0]']         
 seConv2D)                                                                                        
                                                                                                  
 block_3_depthwise_BN (Batc  (None, 28, 28, 144)          576       ['block_3_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_3_depthwise_relu (Re  (None, 28, 28, 144)          0         ['block_3_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_3_project (Conv2D)    (None, 28, 28, 32)           4608      ['block_3_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_3_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_3_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_4_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_3_project_BN[0][0]']  
                                                                                                  
 block_4_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_4_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_4_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_4_expand_BN[0][0]']   
                                                                                                  
 block_4_depthwise (Depthwi  (None, 28, 28, 192)          1728      ['block_4_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_4_depthwise_BN (Batc  (None, 28, 28, 192)          768       ['block_4_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_4_depthwise_relu (Re  (None, 28, 28, 192)          0         ['block_4_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_4_project (Conv2D)    (None, 28, 28, 32)           6144      ['block_4_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_4_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_4_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_4_add (Add)           (None, 28, 28, 32)           0         ['block_3_project_BN[0][0]',  
                                                                     'block_4_project_BN[0][0]']  
                                                                                                  
 block_5_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_4_add[0][0]']         
                                                                                                  
 block_5_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_5_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_5_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_5_expand_BN[0][0]']   
                                                                                                  
 block_5_depthwise (Depthwi  (None, 28, 28, 192)          1728      ['block_5_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_5_depthwise_BN (Batc  (None, 28, 28, 192)          768       ['block_5_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_5_depthwise_relu (Re  (None, 28, 28, 192)          0         ['block_5_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_5_project (Conv2D)    (None, 28, 28, 32)           6144      ['block_5_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_5_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_5_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_5_add (Add)           (None, 28, 28, 32)           0         ['block_4_add[0][0]',         
                                                                     'block_5_project_BN[0][0]']  
                                                                                                  
 block_6_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_5_add[0][0]']         
                                                                                                  
 block_6_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_6_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_6_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_6_expand_BN[0][0]']   
                                                                                                  
 block_6_pad (ZeroPadding2D  (None, 29, 29, 192)          0         ['block_6_expand_relu[0][0]'] 
 )                                                                                                
                                                                                                  
 block_6_depthwise (Depthwi  (None, 14, 14, 192)          1728      ['block_6_pad[0][0]']         

 seConv2D)                                                                                        
                                                                                                  
 block_6_depthwise_BN (Batc  (None, 14, 14, 192)          768       ['block_6_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_6_depthwise_relu (Re  (None, 14, 14, 192)          0         ['block_6_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_6_project (Conv2D)    (None, 14, 14, 64)           12288     ['block_6_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_6_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_6_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_7_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_6_project_BN[0][0]']  
                                                                                                  
 block_7_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_7_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_7_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_7_expand_BN[0][0]']   
                                                                                                  
 block_7_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_7_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_7_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_7_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_7_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_7_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_7_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_7_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_7_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_7_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_7_add (Add)           (None, 14, 14, 64)           0         ['block_6_project_BN[0][0]',  
                                                                     'block_7_project_BN[0][0]']  
                                                                                                  
 block_8_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_7_add[0][0]']         
                                                                                                  
 block_8_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_8_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_8_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_8_expand_BN[0][0]']   
                                                                                                  
 block_8_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_8_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_8_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_8_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_8_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_8_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_8_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_8_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_8_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_8_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_8_add (Add)           (None, 14, 14, 64)           0         ['block_7_add[0][0]',         
                                                                     'block_8_project_BN[0][0]']  
                                                                                                  
 block_9_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_8_add[0][0]']         
                                                                                                  
 block_9_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_9_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_9_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_9_expand_BN[0][0]']   
                                                                                                  
 block_9_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_9_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_9_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_9_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_9_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_9_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_9_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_9_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  

 block_9_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_9_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_9_add (Add)           (None, 14, 14, 64)           0         ['block_8_add[0][0]',         
                                                                     'block_9_project_BN[0][0]']  
                                                                                                  
 block_10_expand (Conv2D)    (None, 14, 14, 384)          24576     ['block_9_add[0][0]']         
                                                                                                  
 block_10_expand_BN (BatchN  (None, 14, 14, 384)          1536      ['block_10_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_10_expand_relu (ReLU  (None, 14, 14, 384)          0         ['block_10_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_10_depthwise (Depthw  (None, 14, 14, 384)          3456      ['block_10_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_10_depthwise_BN (Bat  (None, 14, 14, 384)          1536      ['block_10_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_10_depthwise_relu (R  (None, 14, 14, 384)          0         ['block_10_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_10_project (Conv2D)   (None, 14, 14, 96)           36864     ['block_10_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_10_project_BN (Batch  (None, 14, 14, 96)           384       ['block_10_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_11_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_10_project_BN[0][0]'] 
                                                                                                  
 block_11_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_11_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_11_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_11_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_11_depthwise (Depthw  (None, 14, 14, 576)          5184      ['block_11_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_11_depthwise_BN (Bat  (None, 14, 14, 576)          2304      ['block_11_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_11_depthwise_relu (R  (None, 14, 14, 576)          0         ['block_11_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_11_project (Conv2D)   (None, 14, 14, 96)           55296     ['block_11_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_11_project_BN (Batch  (None, 14, 14, 96)           384       ['block_11_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_11_add (Add)          (None, 14, 14, 96)           0         ['block_10_project_BN[0][0]', 
                                                                     'block_11_project_BN[0][0]'] 
                                                                                                  
 block_12_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_11_add[0][0]']        
                                                                                                  
 block_12_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_12_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_12_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_12_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_12_depthwise (Depthw  (None, 14, 14, 576)          5184      ['block_12_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_12_depthwise_BN (Bat  (None, 14, 14, 576)          2304      ['block_12_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_12_depthwise_relu (R  (None, 14, 14, 576)          0         ['block_12_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_12_project (Conv2D)   (None, 14, 14, 96)           55296     ['block_12_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_12_project_BN (Batch  (None, 14, 14, 96)           384       ['block_12_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_12_add (Add)          (None, 14, 14, 96)           0         ['block_11_add[0][0]',        
                                                                     'block_12_project_BN[0][0]'] 
                                                                                                  
 block_13_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_12_add[0][0]']        
                                                                                                  

 block_13_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_13_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_13_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_13_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_13_pad (ZeroPadding2  (None, 15, 15, 576)          0         ['block_13_expand_relu[0][0]']
 D)                                                                                               
                                                                                                  
 block_13_depthwise (Depthw  (None, 7, 7, 576)            5184      ['block_13_pad[0][0]']        
 iseConv2D)                                                                                       
                                                                                                  
 block_13_depthwise_BN (Bat  (None, 7, 7, 576)            2304      ['block_13_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_13_depthwise_relu (R  (None, 7, 7, 576)            0         ['block_13_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_13_project (Conv2D)   (None, 7, 7, 160)            92160     ['block_13_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_13_project_BN (Batch  (None, 7, 7, 160)            640       ['block_13_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_14_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_13_project_BN[0][0]'] 
                                                                                                  
 block_14_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_14_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_14_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_14_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_14_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_14_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_14_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_14_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_14_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_14_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_14_project (Conv2D)   (None, 7, 7, 160)            153600    ['block_14_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_14_project_BN (Batch  (None, 7, 7, 160)            640       ['block_14_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_14_add (Add)          (None, 7, 7, 160)            0         ['block_13_project_BN[0][0]', 
                                                                     'block_14_project_BN[0][0]'] 
                                                                                                  
 block_15_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_14_add[0][0]']        
                                                                                                  
 block_15_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_15_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_15_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_15_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_15_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_15_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_15_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_15_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_15_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_15_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_15_project (Conv2D)   (None, 7, 7, 160)            153600    ['block_15_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_15_project_BN (Batch  (None, 7, 7, 160)            640       ['block_15_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_15_add (Add)          (None, 7, 7, 160)            0         ['block_14_add[0][0]',        
                                                                     'block_15_project_BN[0][0]'] 
                                                                                                  
 block_16_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_15_add[0][0]']        
                                                                                                  
 block_16_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_16_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_16_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_16_expand_BN[0][0]']  
 )                                                                                                

                                                                                                  
 block_16_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_16_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_16_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_16_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_16_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_16_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_16_project (Conv2D)   (None, 7, 7, 320)            307200    ['block_16_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_16_project_BN (Batch  (None, 7, 7, 320)            1280      ['block_16_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 Conv_1 (Conv2D)             (None, 7, 7, 1280)           409600    ['block_16_project_BN[0][0]'] 
                                                                                                  
 Conv_1_bn (BatchNormalizat  (None, 7, 7, 1280)           5120      ['Conv_1[0][0]']              
 ion)                                                                                             
                                                                                                  
 out_relu (ReLU)             (None, 7, 7, 1280)           0         ['Conv_1_bn[0][0]']           
                                                                                                  
 global_average_pooling2d_2  (None, 1280)                 0         ['out_relu[0][0]']            
  (GlobalAveragePooling2D)                                                                        
                                                                                                  
 dense_8 (Dense)             (None, 1024)                 1311744   ['global_average_pooling2d_2[0
                                                                    ][0]']                        
                                                                                                  
 dense_9 (Dense)             (None, 512)                  524800    ['dense_8[0][0]']             
                                                                                                  
 dense_10 (Dense)            (None, 256)                  131328    ['dense_9[0][0]']             
                                                                                                  
 dense_11 (Dense)            (None, 196)                  50372     ['dense_10[0][0]']            
                                                                                                  
==================================================================================================
Total params: 4276228 (16.31 MB)
Trainable params: 4231412 (16.14 MB)
Non-trainable params: 44816 (175.06 KB)
__________________________________________________________________________________________________
In [44]:
# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('best_model_2_mobilenet.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)


history = model_mobilenetv2.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // BATCH_SIZE,
    epochs=20,
    validation_data=val_generator,
    validation_steps=val_generator.samples // BATCH_SIZE,
    callbacks=[checkpoint]
)

Epoch 1/20
101/101 [==============================] - ETA: 0s - loss: 4.9946 - accuracy: 0.0228
Epoch 1: val_accuracy improved from -inf to 0.01125, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 441s 4s/step - loss: 4.9946 - accuracy: 0.0228 - val_loss: 7.4085 - val_accuracy: 0.0113
Epoch 2/20
101/101 [==============================] - ETA: 0s - loss: 4.0944 - accuracy: 0.0809
Epoch 2: val_accuracy improved from 0.01125 to 0.01250, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 435s 4s/step - loss: 4.0944 - accuracy: 0.0809 - val_loss: 12.1142 - val_accuracy: 0.0125
Epoch 3/20
101/101 [==============================] - ETA: 0s - loss: 3.4097 - accuracy: 0.1451
Epoch 3: val_accuracy improved from 0.01250 to 0.01812, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 434s 4s/step - loss: 3.4097 - accuracy: 0.1451 - val_loss: 9.9266 - val_accuracy: 0.0181
Epoch 4/20
101/101 [==============================] - ETA: 0s - loss: 2.8020 - accuracy: 0.2313
Epoch 4: val_accuracy did not improve from 0.01812
101/101 [==============================] - 434s 4s/step - loss: 2.8020 - accuracy: 0.2313 - val_loss: 17.5716 - val_accuracy: 0.0081
Epoch 5/20
101/101 [==============================] - ETA: 0s - loss: 2.3337 - accuracy: 0.3243
Epoch 5: val_accuracy did not improve from 0.01812
101/101 [==============================] - 436s 4s/step - loss: 2.3337 - accuracy: 0.3243 - val_loss: 19.7500 - val_accuracy: 0.0069
Epoch 6/20
101/101 [==============================] - ETA: 0s - loss: 2.0262 - accuracy: 0.3954
Epoch 6: val_accuracy did not improve from 0.01812
101/101 [==============================] - 437s 4s/step - loss: 2.0262 - accuracy: 0.3954 - val_loss: 26.4659 - val_accuracy: 0.0075
Epoch 7/20
101/101 [==============================] - ETA: 0s - loss: 1.6950 - accuracy: 0.4809
Epoch 7: val_accuracy did not improve from 0.01812
101/101 [==============================] - 437s 4s/step - loss: 1.6950 - accuracy: 0.4809 - val_loss: 24.8243 - val_accuracy: 0.0094
Epoch 8/20
101/101 [==============================] - ETA: 0s - loss: 1.4917 - accuracy: 0.5419
Epoch 8: val_accuracy did not improve from 0.01812
101/101 [==============================] - 434s 4s/step - loss: 1.4917 - accuracy: 0.5419 - val_loss: 18.8756 - val_accuracy: 0.0088
Epoch 9/20
101/101 [==============================] - ETA: 0s - loss: 1.2888 - accuracy: 0.5962
Epoch 9: val_accuracy did not improve from 0.01812
101/101 [==============================] - 436s 4s/step - loss: 1.2888 - accuracy: 0.5962 - val_loss: 14.5594 - val_accuracy: 0.0137
Epoch 10/20
101/101 [==============================] - ETA: 0s - loss: 1.1944 - accuracy: 0.6213
Epoch 10: val_accuracy improved from 0.01812 to 0.02812, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 435s 4s/step - loss: 1.1944 - accuracy: 0.6213 - val_loss: 16.8297 - val_accuracy: 0.0281
Epoch 11/20
101/101 [==============================] - ETA: 0s - loss: 0.9902 - accuracy: 0.6878
Epoch 11: val_accuracy did not improve from 0.02812
101/101 [==============================] - 434s 4s/step - loss: 0.9902 - accuracy: 0.6878 - val_loss: 28.1909 - val_accuracy: 0.0075
Epoch 12/20
101/101 [==============================] - ETA: 0s - loss: 0.9265 - accuracy: 0.7038
Epoch 12: val_accuracy did not improve from 0.02812
101/101 [==============================] - 440s 4s/step - loss: 0.9265 - accuracy: 0.7038 - val_loss: 16.5198 - val_accuracy: 0.0200
Epoch 13/20
101/101 [==============================] - ETA: 0s - loss: 0.8472 - accuracy: 0.7259
Epoch 13: val_accuracy improved from 0.02812 to 0.04062, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 440s 4s/step - loss: 0.8472 - accuracy: 0.7259 - val_loss: 11.8040 - val_accuracy: 0.0406
Epoch 14/20
101/101 [==============================] - ETA: 0s - loss: 0.7524 - accuracy: 0.7555
Epoch 14: val_accuracy did not improve from 0.04062
101/101 [==============================] - 459s 5s/step - loss: 0.7524 - accuracy: 0.7555 - val_loss: 18.4055 - val_accuracy: 0.0200
Epoch 15/20
101/101 [==============================] - ETA: 0s - loss: 0.7045 - accuracy: 0.7712
Epoch 15: val_accuracy did not improve from 0.04062
101/101 [==============================] - 440s 4s/step - loss: 0.7045 - accuracy: 0.7712 - val_loss: 14.9712 - val_accuracy: 0.0331
Epoch 16/20
101/101 [==============================] - ETA: 0s - loss: 0.6467 - accuracy: 0.7903
Epoch 16: val_accuracy improved from 0.04062 to 0.05000, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 436s 4s/step - loss: 0.6467 - accuracy: 0.7903 - val_loss: 10.8843 - val_accuracy: 0.0500
Epoch 17/20
101/101 [==============================] - ETA: 0s - loss: 0.6218 - accuracy: 0.7926
Epoch 17: val_accuracy improved from 0.05000 to 0.06500, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 435s 4s/step - loss: 0.6218 - accuracy: 0.7926 - val_loss: 11.4153 - val_accuracy: 0.0650
Epoch 18/20
101/101 [==============================] - ETA: 0s - loss: 0.5690 - accuracy: 0.8100
Epoch 18: val_accuracy improved from 0.06500 to 0.08125, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 440s 4s/step - loss: 0.5690 - accuracy: 0.8100 - val_loss: 10.6060 - val_accuracy: 0.0812
Epoch 19/20
101/101 [==============================] - ETA: 0s - loss: 0.5615 - accuracy: 0.8132
Epoch 19: val_accuracy improved from 0.08125 to 0.08875, saving model to best_model_2_mobilenet.h5
101/101 [==============================] - 438s 4s/step - loss: 0.5615 - accuracy: 0.8132 - val_loss: 11.4684 - val_accuracy: 0.0887
Epoch 20/20
101/101 [==============================] - ETA: 0s - loss: 0.5054 - accuracy: 0.8312
Epoch 20: val_accuracy did not improve from 0.08875
101/101 [==============================] - 436s 4s/step - loss: 0.5054 - accuracy: 0.8312 - val_loss: 12.8032 - val_accuracy: 0.0644
In [32]:
loaded_model_3 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_2_mobilenet.h5')
In [33]:
evaluation = loaded_model_3.evaluate(val_generator)
print("Validation Accuracy:", evaluation[1])

26/26 [==============================] - 41s 1s/step - loss: 11.4815 - accuracy: 0.0890
Validation Accuracy: 0.08901166170835495
In [ ]:

Model 4 : VGG 19

In [49]:
# Loading pre-trained VGG19 model
base_model_vgg19 = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Adding layers on top of the base model
x = base_model_vgg19.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)  # Additional dense layer
x = Dense(256, activation='relu')(x)  # Additional dense layer
predictions = Dense(196, activation='softmax')(x)  # Output layer, adjust units for your dataset

# Creating the transfer learning model using VGG19 as the base
model_vgg19 = Model(inputs=base_model_vgg19.input, outputs=predictions)

# Freezing the first few layers of the pre-trained VGG19 model
for layer in model_vgg19.layers[:20]:
    layer.trainable = False

# Training the remaining layers
for layer in model_vgg19.layers[20:]:
    layer.trainable = True

model_vgg19.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
model_vgg19.summary()

Model: "model_4"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_13 (InputLayer)       [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
 global_average_pooling2d_4  (None, 512)               0         
  (GlobalAveragePooling2D)                                       
                                                                 
 dense_16 (Dense)            (None, 1024)              525312    
                                                                 
 dense_17 (Dense)            (None, 512)               524800    
                                                                 
 dense_18 (Dense)            (None, 256)               131328    
                                                                 
 dense_19 (Dense)            (None, 196)               50372     
                                                                 
=================================================================
Total params: 21256196 (81.09 MB)
Trainable params: 3591620 (13.70 MB)
Non-trainable params: 17664576 (67.39 MB)
_________________________________________________________________
In [52]:
# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('best_model_vgg19.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)

history_vgg19 = model_vgg19.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // BATCH_SIZE,
    epochs=10,
    validation_data=val_generator,
    validation_steps=val_generator.samples // BATCH_SIZE,
    callbacks=[checkpoint]
)

Epoch 1/10
101/101 [==============================] - ETA: 0s - loss: 5.2771 - accuracy: 0.0057
Epoch 1: val_accuracy improved from -inf to 0.00687, saving model to best_model_vgg19.h5
101/101 [==============================] - 703s 7s/step - loss: 5.2771 - accuracy: 0.0057 - val_loss: 5.2441 - val_accuracy: 0.0069
Epoch 2/10
101/101 [==============================] - ETA: 0s - loss: 4.9301 - accuracy: 0.0180
Epoch 2: val_accuracy improved from 0.00687 to 0.01875, saving model to best_model_vgg19.h5
101/101 [==============================] - 709s 7s/step - loss: 4.9301 - accuracy: 0.0180 - val_loss: 4.7684 - val_accuracy: 0.0188
Epoch 3/10
101/101 [==============================] - ETA: 0s - loss: 4.4746 - accuracy: 0.0370
Epoch 3: val_accuracy improved from 0.01875 to 0.04688, saving model to best_model_vgg19.h5
101/101 [==============================] - 712s 7s/step - loss: 4.4746 - accuracy: 0.0370 - val_loss: 4.2399 - val_accuracy: 0.0469
Epoch 4/10
101/101 [==============================] - ETA: 0s - loss: 4.0829 - accuracy: 0.0704
Epoch 4: val_accuracy improved from 0.04688 to 0.07688, saving model to best_model_vgg19.h5
101/101 [==============================] - 712s 7s/step - loss: 4.0829 - accuracy: 0.0704 - val_loss: 3.9243 - val_accuracy: 0.0769
Epoch 5/10
101/101 [==============================] - ETA: 0s - loss: 3.6582 - accuracy: 0.1204
Epoch 5: val_accuracy improved from 0.07688 to 0.12000, saving model to best_model_vgg19.h5
101/101 [==============================] - 748s 7s/step - loss: 3.6582 - accuracy: 0.1204 - val_loss: 3.6188 - val_accuracy: 0.1200
Epoch 6/10
101/101 [==============================] - ETA: 0s - loss: 3.2929 - accuracy: 0.1656
Epoch 6: val_accuracy improved from 0.12000 to 0.16125, saving model to best_model_vgg19.h5
101/101 [==============================] - 702s 7s/step - loss: 3.2929 - accuracy: 0.1656 - val_loss: 3.3558 - val_accuracy: 0.1612
Epoch 7/10
101/101 [==============================] - ETA: 0s - loss: 2.9517 - accuracy: 0.2242
Epoch 7: val_accuracy improved from 0.16125 to 0.19688, saving model to best_model_vgg19.h5
101/101 [==============================] - 683s 7s/step - loss: 2.9517 - accuracy: 0.2242 - val_loss: 3.1157 - val_accuracy: 0.1969
Epoch 8/10
101/101 [==============================] - ETA: 0s - loss: 2.7031 - accuracy: 0.2674
Epoch 8: val_accuracy improved from 0.19688 to 0.21250, saving model to best_model_vgg19.h5
101/101 [==============================] - 657s 7s/step - loss: 2.7031 - accuracy: 0.2674 - val_loss: 3.0093 - val_accuracy: 0.2125
Epoch 9/10
101/101 [==============================] - ETA: 0s - loss: 2.4723 - accuracy: 0.3218
Epoch 9: val_accuracy improved from 0.21250 to 0.25750, saving model to best_model_vgg19.h5
101/101 [==============================] - 652s 6s/step - loss: 2.4723 - accuracy: 0.3218 - val_loss: 2.8407 - val_accuracy: 0.2575
Epoch 10/10
101/101 [==============================] - ETA: 0s - loss: 2.2448 - accuracy: 0.3706
Epoch 10: val_accuracy improved from 0.25750 to 0.27562, saving model to best_model_vgg19.h5
101/101 [==============================] - 638s 6s/step - loss: 2.2448 - accuracy: 0.3706 - val_loss: 2.7625 - val_accuracy: 0.2756
In [34]:
loaded_model_4 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_vgg19.h5')
In [35]:
evaluation = loaded_model_4.evaluate(val_generator)
print("Validation Accuracy:", evaluation[1])

26/26 [==============================] - 123s 5s/step - loss: 2.7680 - accuracy: 0.2744
Validation Accuracy: 0.2744014859199524
In [ ]:
In [ ]:

Now, lets downsample the Train data and Validation data to 55%

In [39]:
# Downsample train data to 15%
train_data_downsampled, _ = train_test_split(train_data_new, test_size=0.45, random_state=42)

# Downsample validation data to 15%
val_data_downsampled, _ = train_test_split(val_data, test_size=0.45, random_state=42)

# Define data generators for downsampled train and validation data
train_generator_downsampled = train_datagen.flow_from_dataframe(dataframe=train_data_downsampled,
                                                                x_col="Image Location",
                                                                y_col="Class_Labels",
                                                                target_size=IMAGE_SIZE,
                                                                batch_size=BATCH_SIZE,
                                                                class_mode='categorical',
                                                                shuffle=True)

val_generator_downsampled = val_test_datagen.flow_from_dataframe(dataframe=val_data_downsampled,
                                                                  x_col="Image Location",
                                                                  y_col="Class_Labels",
                                                                  target_size=IMAGE_SIZE,
                                                                  batch_size=BATCH_SIZE,
                                                                  class_mode='categorical',
                                                                  shuffle=False)

Found 3583 validated image filenames belonging to 196 classes.
Found 895 validated image filenames belonging to 196 classes.
In [ ]:

1 : ResNet 50

In [29]:
# Loading pre-trained ResNet50 model
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)  # Adding an additional dense layer
x = Dense(256, activation='relu')(x)  # Adding another dense layer
predictions = Dense(num_classes, activation='softmax')(x)  # Output layer, adjusted to num_classes

# Creating the transfer learning model
model_reset50_downsampled = Model(inputs=base_model.input, outputs=predictions)

# Freezing the first 51 layers of the pre-trained model
for layer in model_reset50_downsampled.layers[:51]:
    layer.trainable = False

# Compiling the model
model_reset50_downsampled.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])

# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('best_model_rcnn_downsampled_weights.h5', 
                             monitor='val_accuracy', 
                             save_best_only=True, 
                             mode='max', 
                             save_weights_only=True,  
                             verbose=1)
# Training the model
history_model_reset50_downsampled = model_reset50_downsampled.fit(train_generator_downsampled,
                            steps_per_epoch=len(train_generator_downsampled),
                            epochs=5,
                            validation_data=val_generator_downsampled,
                            validation_steps=len(val_generator_downsampled),
                            callbacks=[checkpoint])

Epoch 1/5
WARNING:tensorflow:From C:\Users\SAIF\anaconda3\Lib\site-packages\keras\src\engine\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.

56/56 [==============================] - ETA: 0s - loss: 5.2999 - accuracy: 0.0036
Epoch 1: val_accuracy improved from -inf to 0.00223, saving model to best_model_rcnn_downsampled_weights.h5
56/56 [==============================] - 533s 9s/step - loss: 5.2999 - accuracy: 0.0036 - val_loss: 15.2874 - val_accuracy: 0.0022
Epoch 2/5
56/56 [==============================] - ETA: 0s - loss: 5.2770 - accuracy: 0.0047
Epoch 2: val_accuracy improved from 0.00223 to 0.00447, saving model to best_model_rcnn_downsampled_weights.h5
56/56 [==============================] - 487s 9s/step - loss: 5.2770 - accuracy: 0.0047 - val_loss: 5.3465 - val_accuracy: 0.0045
Epoch 3/5
56/56 [==============================] - ETA: 0s - loss: 5.2732 - accuracy: 0.0092
Epoch 3: val_accuracy improved from 0.00447 to 0.00782, saving model to best_model_rcnn_downsampled_weights.h5
56/56 [==============================] - 474s 8s/step - loss: 5.2732 - accuracy: 0.0092 - val_loss: 5.2965 - val_accuracy: 0.0078
Epoch 4/5
56/56 [==============================] - ETA: 0s - loss: 5.2711 - accuracy: 0.0081
Epoch 4: val_accuracy did not improve from 0.00782
56/56 [==============================] - 468s 8s/step - loss: 5.2711 - accuracy: 0.0081 - val_loss: 5.3037 - val_accuracy: 0.0078
Epoch 5/5
56/56 [==============================] - ETA: 0s - loss: 5.2688 - accuracy: 0.0070
Epoch 5: val_accuracy did not improve from 0.00782
56/56 [==============================] - 473s 8s/step - loss: 5.2688 - accuracy: 0.0070 - val_loss: 5.2913 - val_accuracy: 0.0078
In [34]:
model_reset50_downsampled.save("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_resnet50_downsampled.h5")
model_reset50_downsampled.save_weights("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_resnet50_downsampled_weights_New.h5")

C:\Users\SAIF\anaconda3\Lib\site-packages\keras\src\engine\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
  saving_api.save_model(
In [41]:
evaluation = loaded_model_5.evaluate(val_generator_downsampled)
print("Validation Accuracy:", evaluation[1])

14/14 [==============================] - 51s 4s/step - loss: 5.3085 - accuracy: 0.0078
Validation Accuracy: 0.007821229286491871
In [ ]:

Model 2 ; Inception Model Containing the following models : {vgg19 + resnet50 + mobilenet + efficientnet}

In [57]:
In [47]:
# Instantiating pre-trained models (VGG19, ResNet50, MobileNet, EfficientNetB0)
vgg19_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
resnet50_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
mobilenet_model = MobileNet(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
efficientnet_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Freezing layers in each pre-trained model
for model in [vgg19_model, resnet50_model, mobilenet_model, efficientnet_model]:
    for layer in model.layers:
        layer.trainable = False

# Defining input layer
input_layer = tf.keras.Input(shape=(224, 224, 3))

# Getting outputs from each pre-trained model
vgg19_output = vgg19_model(input_layer)
resnet50_output = resnet50_model(input_layer)
mobilenet_output = mobilenet_model(input_layer)
efficientnet_output = efficientnet_model(input_layer)

# Concatenating outputs
concatenated_output = Concatenate()([vgg19_output, resnet50_output, mobilenet_output, efficientnet_output])

# Global average pooling and dense layers
x = GlobalAveragePooling2D()(concatenated_output)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
x = Dense(256, activation='relu')(x)
predictions = Dense(196, activation='softmax')(x)  

# Creating the ensemble model
ensemble_model_downscaled = Model(inputs=input_layer, outputs=predictions)
ensemble_model_downscaled.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
ensemble_model_downscaled.summary()

Model: "model_2"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_15 (InputLayer)       [(None, 224, 224, 3)]        0         []                            
                                                                                                  
 vgg19 (Functional)          (None, 7, 7, 512)            2002438   ['input_15[0][0]']            
                                                          4                                       
                                                                                                  
 resnet50 (Functional)       (None, 7, 7, 2048)           2358771   ['input_15[0][0]']            
                                                          2                                       
                                                                                                  
 mobilenet_1.00_224 (Functi  (None, 7, 7, 1024)           3228864   ['input_15[0][0]']            
 onal)                                                                                            
                                                                                                  
 efficientnetb0 (Functional  (None, 7, 7, 1280)           4049571   ['input_15[0][0]']            
 )                                                                                                
                                                                                                  
 concatenate_1 (Concatenate  (None, 7, 7, 4864)           0         ['vgg19[0][0]',               
 )                                                                   'resnet50[0][0]',            
                                                                     'mobilenet_1.00_224[0][0]',  
                                                                     'efficientnetb0[0][0]']      
                                                                                                  
 global_average_pooling2d_2  (None, 4864)                 0         ['concatenate_1[0][0]']       
  (GlobalAveragePooling2D)                                                                        
                                                                                                  
 dense_8 (Dense)             (None, 1024)                 4981760   ['global_average_pooling2d_2[0
                                                                    ][0]']                        
                                                                                                  
 dense_9 (Dense)             (None, 512)                  524800    ['dense_8[0][0]']             
                                                                                                  
 dense_10 (Dense)            (None, 256)                  131328    ['dense_9[0][0]']             
                                                                                                  
 dense_11 (Dense)            (None, 196)                  50372     ['dense_10[0][0]']            
                                                                                                  
==================================================================================================
Total params: 56578791 (215.83 MB)
Trainable params: 5688260 (21.70 MB)
Non-trainable params: 50890531 (194.13 MB)
__________________________________________________________________________________________________
In [48]:
# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('best_model_2_inception_downscaled.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)

early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
history_ensemble_model_downscaleed = ensemble_model_downscaled.fit(
    train_generator_downsampled,
    steps_per_epoch=len(train_generator_downsampled),
    epochs=5,
    validation_data=val_generator_downsampled,
    validation_steps=len(val_generator_downsampled),
    callbacks=[checkpoint, early_stopping] 
)

Epoch 1/5
56/56 [==============================] - ETA: 0s - loss: 5.3306 - accuracy: 0.0070 
Epoch 1: val_accuracy improved from -inf to 0.01006, saving model to best_model_2_inception_downscaled.h5
56/56 [==============================] - 834s 14s/step - loss: 5.3306 - accuracy: 0.0070 - val_loss: 5.2718 - val_accuracy: 0.0101
Epoch 2/5
56/56 [==============================] - ETA: 0s - loss: 5.1879 - accuracy: 0.0087 
Epoch 2: val_accuracy improved from 0.01006 to 0.01564, saving model to best_model_2_inception_downscaled.h5
56/56 [==============================] - 816s 15s/step - loss: 5.1879 - accuracy: 0.0087 - val_loss: 5.0710 - val_accuracy: 0.0156
Epoch 3/5
56/56 [==============================] - ETA: 0s - loss: 4.7738 - accuracy: 0.0248 
Epoch 3: val_accuracy improved from 0.01564 to 0.03799, saving model to best_model_2_inception_downscaled.h5
56/56 [==============================] - 803s 14s/step - loss: 4.7738 - accuracy: 0.0248 - val_loss: 4.5846 - val_accuracy: 0.0380
Epoch 4/5
56/56 [==============================] - ETA: 0s - loss: 4.3104 - accuracy: 0.0514 
Epoch 4: val_accuracy improved from 0.03799 to 0.05922, saving model to best_model_2_inception_downscaled.h5
56/56 [==============================] - 812s 15s/step - loss: 4.3104 - accuracy: 0.0514 - val_loss: 4.2089 - val_accuracy: 0.0592
Epoch 5/5
56/56 [==============================] - ETA: 0s - loss: 3.8903 - accuracy: 0.0862 
Epoch 5: val_accuracy improved from 0.05922 to 0.07039, saving model to best_model_2_inception_downscaled.h5
56/56 [==============================] - 810s 14s/step - loss: 3.8903 - accuracy: 0.0862 - val_loss: 3.9071 - val_accuracy: 0.0704
In [42]:
loaded_model_6 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_2_inception_downscaled.h5')
In [44]:
evaluation = loaded_model_6.evaluate(val_generator_downsampled)
print("Validation Accuracy:", evaluation[1])

14/14 [==============================] - 154s 10s/step - loss: 3.9071 - accuracy: 0.0704
Validation Accuracy: 0.07039105892181396
In [ ]:

Model 3 : Mobile Net

In [52]:
# Loading pre-trained MobileNetV2 model
base_model_mobilenetv2 = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Adding layers on top of the base model
x = base_model_mobilenetv2.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)  # Additional dense layer
x = Dense(256, activation='relu')(x)  # Additional dense layer
predictions = Dense(196, activation='softmax')(x)  # Output layer, adjust units for your dataset

# Creating the transfer learning model using MobileNetV2 as the base
model_mobilenetv2_downsampled = Model(inputs=base_model_mobilenetv2.input, outputs=predictions)

# Freezing the first few layers of the pre-trained MobileNetV2 model
for layer in model_mobilenetv2.layers[:20]:
    layer.trainable = False

# Training the remaining layers
for layer in model_mobilenetv2.layers[20:]:
    layer.trainable = True

model_mobilenetv2_downsampled.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
model_mobilenetv2_downsampled.summary()

Model: "model_4"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_17 (InputLayer)       [(None, 224, 224, 3)]        0         []                            
                                                                                                  
 Conv1 (Conv2D)              (None, 112, 112, 32)         864       ['input_17[0][0]']            
                                                                                                  
 bn_Conv1 (BatchNormalizati  (None, 112, 112, 32)         128       ['Conv1[0][0]']               
 on)                                                                                              
                                                                                                  
 Conv1_relu (ReLU)           (None, 112, 112, 32)         0         ['bn_Conv1[0][0]']            
                                                                                                  
 expanded_conv_depthwise (D  (None, 112, 112, 32)         288       ['Conv1_relu[0][0]']          
 epthwiseConv2D)                                                                                  
                                                                                                  
 expanded_conv_depthwise_BN  (None, 112, 112, 32)         128       ['expanded_conv_depthwise[0][0
  (BatchNormalization)                                              ]']                           
                                                                                                  
 expanded_conv_depthwise_re  (None, 112, 112, 32)         0         ['expanded_conv_depthwise_BN[0
 lu (ReLU)                                                          ][0]']                        
                                                                                                  
 expanded_conv_project (Con  (None, 112, 112, 16)         512       ['expanded_conv_depthwise_relu
 v2D)                                                               [0][0]']                      
                                                                                                  
 expanded_conv_project_BN (  (None, 112, 112, 16)         64        ['expanded_conv_project[0][0]'
 BatchNormalization)                                                ]                             
                                                                                                  
 block_1_expand (Conv2D)     (None, 112, 112, 96)         1536      ['expanded_conv_project_BN[0][
                                                                    0]']                          
                                                                                                  
 block_1_expand_BN (BatchNo  (None, 112, 112, 96)         384       ['block_1_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_1_expand_relu (ReLU)  (None, 112, 112, 96)         0         ['block_1_expand_BN[0][0]']   
                                                                                                  
 block_1_pad (ZeroPadding2D  (None, 113, 113, 96)         0         ['block_1_expand_relu[0][0]'] 
 )                                                                                                
                                                                                                  
 block_1_depthwise (Depthwi  (None, 56, 56, 96)           864       ['block_1_pad[0][0]']         
 seConv2D)                                                                                        
                                                                                                  
 block_1_depthwise_BN (Batc  (None, 56, 56, 96)           384       ['block_1_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_1_depthwise_relu (Re  (None, 56, 56, 96)           0         ['block_1_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_1_project (Conv2D)    (None, 56, 56, 24)           2304      ['block_1_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_1_project_BN (BatchN  (None, 56, 56, 24)           96        ['block_1_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_2_expand (Conv2D)     (None, 56, 56, 144)          3456      ['block_1_project_BN[0][0]']  
                                                                                                  
 block_2_expand_BN (BatchNo  (None, 56, 56, 144)          576       ['block_2_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_2_expand_relu (ReLU)  (None, 56, 56, 144)          0         ['block_2_expand_BN[0][0]']   
                                                                                                  
 block_2_depthwise (Depthwi  (None, 56, 56, 144)          1296      ['block_2_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_2_depthwise_BN (Batc  (None, 56, 56, 144)          576       ['block_2_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_2_depthwise_relu (Re  (None, 56, 56, 144)          0         ['block_2_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_2_project (Conv2D)    (None, 56, 56, 24)           3456      ['block_2_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_2_project_BN (BatchN  (None, 56, 56, 24)           96        ['block_2_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_2_add (Add)           (None, 56, 56, 24)           0         ['block_1_project_BN[0][0]',  
                                                                     'block_2_project_BN[0][0]']  
                                                                                                  
 block_3_expand (Conv2D)     (None, 56, 56, 144)          3456      ['block_2_add[0][0]']         
                                                                                                  
 block_3_expand_BN (BatchNo  (None, 56, 56, 144)          576       ['block_3_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  

 block_3_expand_relu (ReLU)  (None, 56, 56, 144)          0         ['block_3_expand_BN[0][0]']   
                                                                                                  
 block_3_pad (ZeroPadding2D  (None, 57, 57, 144)          0         ['block_3_expand_relu[0][0]'] 
 )                                                                                                
                                                                                                  
 block_3_depthwise (Depthwi  (None, 28, 28, 144)          1296      ['block_3_pad[0][0]']         
 seConv2D)                                                                                        
                                                                                                  
 block_3_depthwise_BN (Batc  (None, 28, 28, 144)          576       ['block_3_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_3_depthwise_relu (Re  (None, 28, 28, 144)          0         ['block_3_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_3_project (Conv2D)    (None, 28, 28, 32)           4608      ['block_3_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_3_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_3_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_4_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_3_project_BN[0][0]']  
                                                                                                  
 block_4_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_4_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_4_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_4_expand_BN[0][0]']   
                                                                                                  
 block_4_depthwise (Depthwi  (None, 28, 28, 192)          1728      ['block_4_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_4_depthwise_BN (Batc  (None, 28, 28, 192)          768       ['block_4_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_4_depthwise_relu (Re  (None, 28, 28, 192)          0         ['block_4_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_4_project (Conv2D)    (None, 28, 28, 32)           6144      ['block_4_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_4_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_4_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_4_add (Add)           (None, 28, 28, 32)           0         ['block_3_project_BN[0][0]',  
                                                                     'block_4_project_BN[0][0]']  
                                                                                                  
 block_5_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_4_add[0][0]']         
                                                                                                  
 block_5_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_5_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_5_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_5_expand_BN[0][0]']   
                                                                                                  
 block_5_depthwise (Depthwi  (None, 28, 28, 192)          1728      ['block_5_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_5_depthwise_BN (Batc  (None, 28, 28, 192)          768       ['block_5_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_5_depthwise_relu (Re  (None, 28, 28, 192)          0         ['block_5_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_5_project (Conv2D)    (None, 28, 28, 32)           6144      ['block_5_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_5_project_BN (BatchN  (None, 28, 28, 32)           128       ['block_5_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_5_add (Add)           (None, 28, 28, 32)           0         ['block_4_add[0][0]',         
                                                                     'block_5_project_BN[0][0]']  
                                                                                                  
 block_6_expand (Conv2D)     (None, 28, 28, 192)          6144      ['block_5_add[0][0]']         
                                                                                                  
 block_6_expand_BN (BatchNo  (None, 28, 28, 192)          768       ['block_6_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_6_expand_relu (ReLU)  (None, 28, 28, 192)          0         ['block_6_expand_BN[0][0]']   
                                                                                                  
 block_6_pad (ZeroPadding2D  (None, 29, 29, 192)          0         ['block_6_expand_relu[0][0]'] 
 )                                                                                                
                                                                                                  
 block_6_depthwise (Depthwi  (None, 14, 14, 192)          1728      ['block_6_pad[0][0]']         
 seConv2D)                                                                                        
                                                                                                  

 block_6_depthwise_BN (Batc  (None, 14, 14, 192)          768       ['block_6_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_6_depthwise_relu (Re  (None, 14, 14, 192)          0         ['block_6_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_6_project (Conv2D)    (None, 14, 14, 64)           12288     ['block_6_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_6_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_6_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_7_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_6_project_BN[0][0]']  
                                                                                                  
 block_7_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_7_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_7_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_7_expand_BN[0][0]']   
                                                                                                  
 block_7_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_7_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_7_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_7_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_7_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_7_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_7_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_7_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_7_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_7_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_7_add (Add)           (None, 14, 14, 64)           0         ['block_6_project_BN[0][0]',  
                                                                     'block_7_project_BN[0][0]']  
                                                                                                  
 block_8_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_7_add[0][0]']         
                                                                                                  
 block_8_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_8_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_8_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_8_expand_BN[0][0]']   
                                                                                                  
 block_8_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_8_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_8_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_8_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_8_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_8_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_8_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_8_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_8_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_8_project[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_8_add (Add)           (None, 14, 14, 64)           0         ['block_7_add[0][0]',         
                                                                     'block_8_project_BN[0][0]']  
                                                                                                  
 block_9_expand (Conv2D)     (None, 14, 14, 384)          24576     ['block_8_add[0][0]']         
                                                                                                  
 block_9_expand_BN (BatchNo  (None, 14, 14, 384)          1536      ['block_9_expand[0][0]']      
 rmalization)                                                                                     
                                                                                                  
 block_9_expand_relu (ReLU)  (None, 14, 14, 384)          0         ['block_9_expand_BN[0][0]']   
                                                                                                  
 block_9_depthwise (Depthwi  (None, 14, 14, 384)          3456      ['block_9_expand_relu[0][0]'] 
 seConv2D)                                                                                        
                                                                                                  
 block_9_depthwise_BN (Batc  (None, 14, 14, 384)          1536      ['block_9_depthwise[0][0]']   
 hNormalization)                                                                                  
                                                                                                  
 block_9_depthwise_relu (Re  (None, 14, 14, 384)          0         ['block_9_depthwise_BN[0][0]']
 LU)                                                                                              
                                                                                                  
 block_9_project (Conv2D)    (None, 14, 14, 64)           24576     ['block_9_depthwise_relu[0][0]
                                                                    ']                            
                                                                                                  
 block_9_project_BN (BatchN  (None, 14, 14, 64)           256       ['block_9_project[0][0]']     
 ormalization)                                                                                    

                                                                                                  
 block_9_add (Add)           (None, 14, 14, 64)           0         ['block_8_add[0][0]',         
                                                                     'block_9_project_BN[0][0]']  
                                                                                                  
 block_10_expand (Conv2D)    (None, 14, 14, 384)          24576     ['block_9_add[0][0]']         
                                                                                                  
 block_10_expand_BN (BatchN  (None, 14, 14, 384)          1536      ['block_10_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_10_expand_relu (ReLU  (None, 14, 14, 384)          0         ['block_10_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_10_depthwise (Depthw  (None, 14, 14, 384)          3456      ['block_10_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_10_depthwise_BN (Bat  (None, 14, 14, 384)          1536      ['block_10_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_10_depthwise_relu (R  (None, 14, 14, 384)          0         ['block_10_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_10_project (Conv2D)   (None, 14, 14, 96)           36864     ['block_10_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_10_project_BN (Batch  (None, 14, 14, 96)           384       ['block_10_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_11_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_10_project_BN[0][0]'] 
                                                                                                  
 block_11_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_11_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_11_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_11_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_11_depthwise (Depthw  (None, 14, 14, 576)          5184      ['block_11_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_11_depthwise_BN (Bat  (None, 14, 14, 576)          2304      ['block_11_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_11_depthwise_relu (R  (None, 14, 14, 576)          0         ['block_11_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_11_project (Conv2D)   (None, 14, 14, 96)           55296     ['block_11_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_11_project_BN (Batch  (None, 14, 14, 96)           384       ['block_11_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_11_add (Add)          (None, 14, 14, 96)           0         ['block_10_project_BN[0][0]', 
                                                                     'block_11_project_BN[0][0]'] 
                                                                                                  
 block_12_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_11_add[0][0]']        
                                                                                                  
 block_12_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_12_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_12_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_12_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_12_depthwise (Depthw  (None, 14, 14, 576)          5184      ['block_12_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_12_depthwise_BN (Bat  (None, 14, 14, 576)          2304      ['block_12_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_12_depthwise_relu (R  (None, 14, 14, 576)          0         ['block_12_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_12_project (Conv2D)   (None, 14, 14, 96)           55296     ['block_12_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_12_project_BN (Batch  (None, 14, 14, 96)           384       ['block_12_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_12_add (Add)          (None, 14, 14, 96)           0         ['block_11_add[0][0]',        
                                                                     'block_12_project_BN[0][0]'] 
                                                                                                  
 block_13_expand (Conv2D)    (None, 14, 14, 576)          55296     ['block_12_add[0][0]']        
                                                                                                  
 block_13_expand_BN (BatchN  (None, 14, 14, 576)          2304      ['block_13_expand[0][0]']     
 ormalization)                                                                                    

                                                                                                  
 block_13_expand_relu (ReLU  (None, 14, 14, 576)          0         ['block_13_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_13_pad (ZeroPadding2  (None, 15, 15, 576)          0         ['block_13_expand_relu[0][0]']
 D)                                                                                               
                                                                                                  
 block_13_depthwise (Depthw  (None, 7, 7, 576)            5184      ['block_13_pad[0][0]']        
 iseConv2D)                                                                                       
                                                                                                  
 block_13_depthwise_BN (Bat  (None, 7, 7, 576)            2304      ['block_13_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_13_depthwise_relu (R  (None, 7, 7, 576)            0         ['block_13_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_13_project (Conv2D)   (None, 7, 7, 160)            92160     ['block_13_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_13_project_BN (Batch  (None, 7, 7, 160)            640       ['block_13_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_14_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_13_project_BN[0][0]'] 
                                                                                                  
 block_14_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_14_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_14_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_14_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_14_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_14_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_14_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_14_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_14_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_14_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_14_project (Conv2D)   (None, 7, 7, 160)            153600    ['block_14_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_14_project_BN (Batch  (None, 7, 7, 160)            640       ['block_14_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_14_add (Add)          (None, 7, 7, 160)            0         ['block_13_project_BN[0][0]', 
                                                                     'block_14_project_BN[0][0]'] 
                                                                                                  
 block_15_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_14_add[0][0]']        
                                                                                                  
 block_15_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_15_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_15_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_15_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_15_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_15_expand_relu[0][0]']
 iseConv2D)                                                                                       
                                                                                                  
 block_15_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_15_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_15_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_15_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_15_project (Conv2D)   (None, 7, 7, 160)            153600    ['block_15_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_15_project_BN (Batch  (None, 7, 7, 160)            640       ['block_15_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 block_15_add (Add)          (None, 7, 7, 160)            0         ['block_14_add[0][0]',        
                                                                     'block_15_project_BN[0][0]'] 
                                                                                                  
 block_16_expand (Conv2D)    (None, 7, 7, 960)            153600    ['block_15_add[0][0]']        
                                                                                                  
 block_16_expand_BN (BatchN  (None, 7, 7, 960)            3840      ['block_16_expand[0][0]']     
 ormalization)                                                                                    
                                                                                                  
 block_16_expand_relu (ReLU  (None, 7, 7, 960)            0         ['block_16_expand_BN[0][0]']  
 )                                                                                                
                                                                                                  
 block_16_depthwise (Depthw  (None, 7, 7, 960)            8640      ['block_16_expand_relu[0][0]']

 iseConv2D)                                                                                       
                                                                                                  
 block_16_depthwise_BN (Bat  (None, 7, 7, 960)            3840      ['block_16_depthwise[0][0]']  
 chNormalization)                                                                                 
                                                                                                  
 block_16_depthwise_relu (R  (None, 7, 7, 960)            0         ['block_16_depthwise_BN[0][0]'
 eLU)                                                               ]                             
                                                                                                  
 block_16_project (Conv2D)   (None, 7, 7, 320)            307200    ['block_16_depthwise_relu[0][0
                                                                    ]']                           
                                                                                                  
 block_16_project_BN (Batch  (None, 7, 7, 320)            1280      ['block_16_project[0][0]']    
 Normalization)                                                                                   
                                                                                                  
 Conv_1 (Conv2D)             (None, 7, 7, 1280)           409600    ['block_16_project_BN[0][0]'] 
                                                                                                  
 Conv_1_bn (BatchNormalizat  (None, 7, 7, 1280)           5120      ['Conv_1[0][0]']              
 ion)                                                                                             
                                                                                                  
 out_relu (ReLU)             (None, 7, 7, 1280)           0         ['Conv_1_bn[0][0]']           
                                                                                                  
 global_average_pooling2d_4  (None, 1280)                 0         ['out_relu[0][0]']            
  (GlobalAveragePooling2D)                                                                        
                                                                                                  
 dense_16 (Dense)            (None, 1024)                 1311744   ['global_average_pooling2d_4[0
                                                                    ][0]']                        
                                                                                                  
 dense_17 (Dense)            (None, 512)                  524800    ['dense_16[0][0]']            
                                                                                                  
 dense_18 (Dense)            (None, 256)                  131328    ['dense_17[0][0]']            
                                                                                                  
 dense_19 (Dense)            (None, 196)                  50372     ['dense_18[0][0]']            
                                                                                                  
==================================================================================================
Total params: 4276228 (16.31 MB)
Trainable params: 4242116 (16.18 MB)
Non-trainable params: 34112 (133.25 KB)
__________________________________________________________________________________________________
In [53]:
# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('mobilenet_downsampled.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)


history_downsampled = model_mobilenetv2_downsampled.fit(
    train_generator_downsampled,
    steps_per_epoch=train_generator_downsampled.samples // BATCH_SIZE,
    epochs=20,
    validation_data=val_generator_downsampled,
    validation_steps=val_generator_downsampled.samples // BATCH_SIZE,
    callbacks=[checkpoint]
)

Epoch 1/20
55/55 [==============================] - ETA: 0s - loss: 5.2712 - accuracy: 0.0114
Epoch 1: val_accuracy improved from -inf to 0.00481, saving model to mobilenet_downsampled.h5
55/55 [==============================] - 378s 6s/step - loss: 5.2712 - accuracy: 0.0114 - val_loss: 5.9426 - val_accuracy: 0.0048
Epoch 2/20
55/55 [==============================] - ETA: 0s - loss: 4.9177 - accuracy: 0.0296
Epoch 2: val_accuracy improved from 0.00481 to 0.01202, saving model to mobilenet_downsampled.h5
55/55 [==============================] - 320s 6s/step - loss: 4.9177 - accuracy: 0.0296 - val_loss: 7.9961 - val_accuracy: 0.0120
Epoch 3/20
55/55 [==============================] - ETA: 0s - loss: 4.3786 - accuracy: 0.0551
Epoch 3: val_accuracy did not improve from 0.01202
55/55 [==============================] - 329s 6s/step - loss: 4.3786 - accuracy: 0.0551 - val_loss: 13.5448 - val_accuracy: 0.0096
Epoch 4/20
55/55 [==============================] - ETA: 0s - loss: 3.8233 - accuracy: 0.1074
Epoch 4: val_accuracy did not improve from 0.01202
55/55 [==============================] - 322s 6s/step - loss: 3.8233 - accuracy: 0.1074 - val_loss: 11.2740 - val_accuracy: 0.0084
Epoch 5/20
55/55 [==============================] - ETA: 0s - loss: 3.4723 - accuracy: 0.1435
Epoch 5: val_accuracy did not improve from 0.01202
55/55 [==============================] - 322s 6s/step - loss: 3.4723 - accuracy: 0.1435 - val_loss: 12.0682 - val_accuracy: 0.0096
Epoch 6/20
55/55 [==============================] - ETA: 0s - loss: 3.0329 - accuracy: 0.1964
Epoch 6: val_accuracy improved from 0.01202 to 0.02284, saving model to mobilenet_downsampled.h5
55/55 [==============================] - 339s 6s/step - loss: 3.0329 - accuracy: 0.1964 - val_loss: 7.9946 - val_accuracy: 0.0228
Epoch 7/20
55/55 [==============================] - ETA: 0s - loss: 2.7196 - accuracy: 0.2560
Epoch 7: val_accuracy did not improve from 0.02284
55/55 [==============================] - 332s 6s/step - loss: 2.7196 - accuracy: 0.2560 - val_loss: 14.1250 - val_accuracy: 0.0168
Epoch 8/20
55/55 [==============================] - ETA: 0s - loss: 2.3873 - accuracy: 0.3035
Epoch 8: val_accuracy did not improve from 0.02284
55/55 [==============================] - 325s 6s/step - loss: 2.3873 - accuracy: 0.3035 - val_loss: 20.2338 - val_accuracy: 0.0072
Epoch 9/20
55/55 [==============================] - ETA: 0s - loss: 2.1929 - accuracy: 0.3569
Epoch 9: val_accuracy did not improve from 0.02284
55/55 [==============================] - 320s 6s/step - loss: 2.1929 - accuracy: 0.3569 - val_loss: 11.9465 - val_accuracy: 0.0132
Epoch 10/20
55/55 [==============================] - ETA: 0s - loss: 1.9748 - accuracy: 0.4066
Epoch 10: val_accuracy did not improve from 0.02284
55/55 [==============================] - 318s 6s/step - loss: 1.9748 - accuracy: 0.4066 - val_loss: 7.5225 - val_accuracy: 0.0180
Epoch 11/20
55/55 [==============================] - ETA: 0s - loss: 1.7596 - accuracy: 0.4584
Epoch 11: val_accuracy did not improve from 0.02284
55/55 [==============================] - 317s 6s/step - loss: 1.7596 - accuracy: 0.4584 - val_loss: 19.9063 - val_accuracy: 0.0024
Epoch 12/20
55/55 [==============================] - ETA: 0s - loss: 1.5768 - accuracy: 0.4996
Epoch 12: val_accuracy did not improve from 0.02284
55/55 [==============================] - 318s 6s/step - loss: 1.5768 - accuracy: 0.4996 - val_loss: 13.9207 - val_accuracy: 0.0156
Epoch 13/20
55/55 [==============================] - ETA: 0s - loss: 1.4003 - accuracy: 0.5669
Epoch 13: val_accuracy did not improve from 0.02284
55/55 [==============================] - 320s 6s/step - loss: 1.4003 - accuracy: 0.5669 - val_loss: 13.4209 - val_accuracy: 0.0144
Epoch 14/20
55/55 [==============================] - ETA: 0s - loss: 1.3152 - accuracy: 0.5845
Epoch 14: val_accuracy did not improve from 0.02284
55/55 [==============================] - 324s 6s/step - loss: 1.3152 - accuracy: 0.5845 - val_loss: 30.5956 - val_accuracy: 0.0060
Epoch 15/20
55/55 [==============================] - ETA: 0s - loss: 1.1308 - accuracy: 0.6411
Epoch 15: val_accuracy did not improve from 0.02284
55/55 [==============================] - 322s 6s/step - loss: 1.1308 - accuracy: 0.6411 - val_loss: 14.3607 - val_accuracy: 0.0144
Epoch 16/20
55/55 [==============================] - ETA: 0s - loss: 1.0339 - accuracy: 0.6738
Epoch 16: val_accuracy did not improve from 0.02284
55/55 [==============================] - 319s 6s/step - loss: 1.0339 - accuracy: 0.6738 - val_loss: 28.2342 - val_accuracy: 0.0084
Epoch 17/20
55/55 [==============================] - ETA: 0s - loss: 0.9948 - accuracy: 0.6800
Epoch 17: val_accuracy improved from 0.02284 to 0.02644, saving model to mobilenet_downsampled.h5
55/55 [==============================] - 323s 6s/step - loss: 0.9948 - accuracy: 0.6800 - val_loss: 10.5286 - val_accuracy: 0.0264
Epoch 18/20
55/55 [==============================] - ETA: 0s - loss: 0.9363 - accuracy: 0.6982
Epoch 18: val_accuracy improved from 0.02644 to 0.03125, saving model to mobilenet_downsampled.h5
55/55 [==============================] - 328s 6s/step - loss: 0.9363 - accuracy: 0.6982 - val_loss: 14.6759 - val_accuracy: 0.0312
Epoch 19/20
55/55 [==============================] - ETA: 0s - loss: 0.8245 - accuracy: 0.7221
Epoch 19: val_accuracy did not improve from 0.03125
55/55 [==============================] - 323s 6s/step - loss: 0.8245 - accuracy: 0.7221 - val_loss: 15.8126 - val_accuracy: 0.0180
Epoch 20/20
55/55 [==============================] - ETA: 0s - loss: 0.7895 - accuracy: 0.7494
Epoch 20: val_accuracy did not improve from 0.03125
55/55 [==============================] - 320s 6s/step - loss: 0.7895 - accuracy: 0.7494 - val_loss: 13.9829 - val_accuracy: 0.0096
In [45]:
loaded_model_7 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/mobilenet_downsampled.h5')
In [46]:
evaluation = loaded_model_7.evaluate(val_generator_downsampled)
print("Validation Accuracy:", evaluation[1])

14/14 [==============================] - 24s 1s/step - loss: 14.7977 - accuracy: 0.0291
Validation Accuracy: 0.029050279408693314
In [ ]:

Model 4 : VGG 19

In [55]:
# Loading pre-trained VGG19 model
base_model_vgg19 = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Adding layers on top of the base model
x = base_model_vgg19.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)  # Additional dense layer
x = Dense(256, activation='relu')(x)  # Additional dense layer
predictions = Dense(196, activation='softmax')(x)  # Output layer, adjust units for your dataset

# Creating the transfer learning model using VGG19 as the base
model_vgg19_downsampled = Model(inputs=base_model_vgg19.input, outputs=predictions)

# Freezing the first few layers of the pre-trained VGG19 model
for layer in model_vgg19_downsampled.layers[:20]:
    layer.trainable = False

# Training the remaining layers
for layer in model_vgg19_downsampled.layers[20:]:
    layer.trainable = True

model_vgg19_downsampled.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
model_vgg19_downsampled.summary()

Model: "model_6"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_19 (InputLayer)       [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
 global_average_pooling2d_6  (None, 512)               0         
  (GlobalAveragePooling2D)                                       
                                                                 
 dense_24 (Dense)            (None, 1024)              525312    
                                                                 
 dense_25 (Dense)            (None, 512)               524800    
                                                                 
 dense_26 (Dense)            (None, 256)               131328    
                                                                 
 dense_27 (Dense)            (None, 196)               50372     
                                                                 
=================================================================
Total params: 21256196 (81.09 MB)
Trainable params: 3591620 (13.70 MB)
Non-trainable params: 17664576 (67.39 MB)
_________________________________________________________________
In [59]:
# Defining checkpoint to save the best model
checkpoint = ModelCheckpoint('best_model_vgg19.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)

history_vgg19_downsampled = model_vgg19_downsampled.fit(
    train_generator_downsampled,
    steps_per_epoch=train_generator_downsampled.samples // BATCH_SIZE,
    epochs=10,
    validation_data=val_generator_downsampled,
    validation_steps=val_generator_downsampled.samples // BATCH_SIZE,
    callbacks=[checkpoint]
)

Epoch 1/10
55/55 [==============================] - ETA: 0s - loss: 5.2966 - accuracy: 0.0060
Epoch 1: val_accuracy improved from -inf to 0.00841, saving model to best_model_vgg19.h5
55/55 [==============================] - 379s 7s/step - loss: 5.2966 - accuracy: 0.0060 - val_loss: 5.2774 - val_accuracy: 0.0084
Epoch 2/10
55/55 [==============================] - ETA: 0s - loss: 5.2572 - accuracy: 0.0111
Epoch 2: val_accuracy improved from 0.00841 to 0.01683, saving model to best_model_vgg19.h5
55/55 [==============================] - 363s 7s/step - loss: 5.2572 - accuracy: 0.0111 - val_loss: 5.2375 - val_accuracy: 0.0168
Epoch 3/10
55/55 [==============================] - ETA: 0s - loss: 5.0520 - accuracy: 0.0182
Epoch 3: val_accuracy did not improve from 0.01683
55/55 [==============================] - 358s 7s/step - loss: 5.0520 - accuracy: 0.0182 - val_loss: 5.1003 - val_accuracy: 0.0120
Epoch 4/10
55/55 [==============================] - ETA: 0s - loss: 4.6161 - accuracy: 0.0378
Epoch 4: val_accuracy improved from 0.01683 to 0.03005, saving model to best_model_vgg19.h5
55/55 [==============================] - 355s 6s/step - loss: 4.6161 - accuracy: 0.0378 - val_loss: 4.5393 - val_accuracy: 0.0300
Epoch 5/10
55/55 [==============================] - ETA: 0s - loss: 4.2666 - accuracy: 0.0534
Epoch 5: val_accuracy improved from 0.03005 to 0.03606, saving model to best_model_vgg19.h5
55/55 [==============================] - 359s 7s/step - loss: 4.2666 - accuracy: 0.0534 - val_loss: 4.3504 - val_accuracy: 0.0361
Epoch 6/10
55/55 [==============================] - ETA: 0s - loss: 3.9143 - accuracy: 0.0872
Epoch 6: val_accuracy improved from 0.03606 to 0.06490, saving model to best_model_vgg19.h5
55/55 [==============================] - 358s 7s/step - loss: 3.9143 - accuracy: 0.0872 - val_loss: 4.1157 - val_accuracy: 0.0649
Epoch 7/10
55/55 [==============================] - ETA: 0s - loss: 3.6146 - accuracy: 0.1270
Epoch 7: val_accuracy improved from 0.06490 to 0.09014, saving model to best_model_vgg19.h5
55/55 [==============================] - 350s 6s/step - loss: 3.6146 - accuracy: 0.1270 - val_loss: 3.9002 - val_accuracy: 0.0901
Epoch 8/10
55/55 [==============================] - ETA: 0s - loss: 3.3387 - accuracy: 0.1660
Epoch 8: val_accuracy improved from 0.09014 to 0.12380, saving model to best_model_vgg19.h5
55/55 [==============================] - 343s 6s/step - loss: 3.3387 - accuracy: 0.1660 - val_loss: 3.6965 - val_accuracy: 0.1238
Epoch 9/10
55/55 [==============================] - ETA: 0s - loss: 3.1302 - accuracy: 0.1964
Epoch 9: val_accuracy improved from 0.12380 to 0.12740, saving model to best_model_vgg19.h5
55/55 [==============================] - 342s 6s/step - loss: 3.1302 - accuracy: 0.1964 - val_loss: 3.6052 - val_accuracy: 0.1274
Epoch 10/10
55/55 [==============================] - ETA: 0s - loss: 2.8652 - accuracy: 0.2492
Epoch 10: val_accuracy improved from 0.12740 to 0.15264, saving model to best_model_vgg19.h5
55/55 [==============================] - 371s 7s/step - loss: 2.8652 - accuracy: 0.2492 - val_loss: 3.4049 - val_accuracy: 0.1526
In [47]:
loaded_model_8 = load_model('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/best_model_vgg19_downsampled.h5')
In [48]:
evaluation = loaded_model_8.evaluate(val_generator_downsampled)
print("Validation Accuracy:", evaluation[1])

14/14 [==============================] - 70s 5s/step - loss: 3.3899 - accuracy: 0.1542
Validation Accuracy: 0.15418994426727295
In [ ]:
Now, lets test how all of our models performed on the Validation as well as Test Dataset
In [54]:
validation_loss_1, validation_acc_1 = loaded_model_1.evaluate(val_generator)
print("Validation Accuracy for Model 1 : Res Net 50:", validation_acc_1)
print("Validation Loss:", validation_loss_1)

validation_loss_2, validation_acc_2 = loaded_model_2.evaluate(val_generator)
print("Validation Accuracy for Model 2 : Inception Model:", validation_acc_2)
print("Validation Loss:", validation_loss_2)

validation_loss_3, validation_acc_3 = loaded_model_3.evaluate(val_generator)
print("Validation Accuracy for Model 3 : MobileNet:", validation_acc_3)
print("Validation Loss:", validation_loss_3)

validation_loss_4, validation_acc_4 = loaded_model_4.evaluate(val_generator)
print("Validation Accuracy for Model 4 : VGG:", validation_acc_4)
print("Validation Loss:", validation_loss_4)

validation_loss_5, validation_acc_5 = loaded_model_5.evaluate(val_generator_downsampled)
print("Validation Accuracy for Model 5 : Res Net 50_downsampled:", validation_acc_5)
print("Validation Loss:", validation_loss_5)

validation_loss_6, validation_acc_6 = loaded_model_6.evaluate(val_generator_downsampled)
print("Validation Accuracy for Model 6 : Inception Model_downsampled:", validation_acc_6)
print("Validation Loss:", validation_loss_6)

validation_loss_7, validation_acc_7 = loaded_model_7.evaluate(val_generator_downsampled)
print("Validation Accuracy for Model 7 : MobileNet_downsampled:", validation_acc_7)
print("Validation Loss:", validation_loss_7)

validation_loss_8, validation_acc_8 = loaded_model_8.evaluate(val_generator_downsampled)
print("Validation Accuracy for Model 8 : VGG_downsampled:", validation_acc_8)
print("Validation Loss:", validation_loss_8)

26/26 [==============================] - 96s 4s/step - loss: 5.3199 - accuracy: 0.0074
Validation Accuracy for Model 1 : Res Net 50: 0.007366482634097338
Validation Loss: 5.319899559020996
26/26 [==============================] - 354s 12s/step - loss: 3.5215 - accuracy: 0.1160
Validation Accuracy for Model 2 : Inception Model: 0.11602210253477097
Validation Loss: 3.5215110778808594
26/26 [==============================] - 39s 1s/step - loss: 11.4815 - accuracy: 0.0890
Validation Accuracy for Model 3 : MobileNet: 0.08901166170835495
Validation Loss: 11.481486320495605
26/26 [==============================] - 131s 5s/step - loss: 2.7680 - accuracy: 0.2744
Validation Accuracy for Model 4 : VGG: 0.2744014859199524
Validation Loss: 2.768026828765869
14/14 [==============================] - 52s 4s/step - loss: 5.3085 - accuracy: 0.0078
Validation Accuracy for Model 5 : Res Net 50_downsampled: 0.007821229286491871
Validation Loss: 5.308520793914795
14/14 [==============================] - 155s 11s/step - loss: 3.9071 - accuracy: 0.0704
Validation Accuracy for Model 6 : Inception Model_downsampled: 0.07039105892181396
Validation Loss: 3.9071359634399414
14/14 [==============================] - 22s 2s/step - loss: 14.7977 - accuracy: 0.0291
Validation Accuracy for Model 7 : MobileNet_downsampled: 0.029050279408693314
Validation Loss: 14.797679901123047
14/14 [==============================] - 73s 5s/step - loss: 3.3899 - accuracy: 0.1542
Validation Accuracy for Model 8 : VGG_downsampled: 0.15418994426727295
Validation Loss: 3.389874219894409
In [55]:
test_data_dir = 'C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Pre requisites/car_data_extracted/Car Images/Test Images'
img_width, img_height = 224, 224
batch_size = 16
validation_classes = sorted(os.listdir(test_data_dir))

test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    classes=validation_classes
)
test_loss_1, test_acc_1 = loaded_model_1.evaluate(test_generator)
print("Test Accuracy for Model 1 : Res Net 50:", test_acc_1)
print("Test Loss:", test_loss_1)

test_loss_2, test_acc_2 = loaded_model_2.evaluate(test_generator)
print("Test Accuracy for Model 2 : Inception Model:", test_acc_2)
print("Test Loss:", test_loss_2)

test_loss_3, test_acc_3 = loaded_model_3.evaluate(test_generator)
print("Test Accuracy for Model 3 : MobileNet:", test_acc_3)
print("Test Loss:", test_loss_3)

test_loss_4, test_acc_4 = loaded_model_4.evaluate(test_generator)
print("Test Accuracy for Model 4 : VGG:", test_acc_4)
print("Test Loss:", test_loss_4)

test_loss_5, test_acc_5 = loaded_model_5.evaluate(test_generator)
print("Test Accuracy for Model 5 : Res Net 50_downsampled:", test_acc_5)
print("Test Loss:", test_loss_5)

test_loss_6, test_acc_6 = loaded_model_6.evaluate(test_generator)
print("Test Accuracy for Model 6 : Inception Model_downsampled:", test_acc_6)
print("Test Loss:", test_loss_6)

test_loss_7, test_acc_7 = loaded_model_7.evaluate(test_generator)
print("Test Accuracy for Model 7 : MobileNet_downsampled:", test_acc_7)
print("Test Loss:", test_loss_7)

test_loss_8, test_acc_8 = loaded_model_8.evaluate(test_generator)
print("Test Accuracy for Model 8 : VGG_downsampled:", test_acc_8)
print("Test Loss:", test_loss_8)

Found 8041 images belonging to 196 classes.
503/503 [==============================] - 498s 989ms/step - loss: 5.3018 - accuracy: 0.0049
Test Accuracy for Model 1 : Res Net 50: 0.004850143101066351
Test Loss: 5.301844596862793
503/503 [==============================] - 1414s 3s/step - loss: 10.3364 - accuracy: 0.0053
Test Accuracy for Model 2 : Inception Model: 0.005347593687474728
Test Loss: 10.336355209350586
503/503 [==============================] - 182s 362ms/step - loss: 26.7246 - accuracy: 0.0019
Test Accuracy for Model 3 : MobileNet: 0.0018654395826160908
Test Loss: 26.7246150970459
503/503 [==============================] - 654s 1s/step - loss: 15.7486 - accuracy: 0.0067
Test Accuracy for Model 4 : VGG: 0.006715582683682442
Test Loss: 15.748576164245605
503/503 [==============================] - 496s 985ms/step - loss: 5.2950 - accuracy: 0.0049
Test Accuracy for Model 5 : Res Net 50_downsampled: 0.004850143101066351
Test Loss: 5.295017242431641
503/503 [==============================] - 1414s 3s/step - loss: 8.7449 - accuracy: 0.0060
Test Accuracy for Model 6 : Inception Model_downsampled: 0.005969406571239233
Test Loss: 8.744937896728516
503/503 [==============================] - 182s 363ms/step - loss: 20.4751 - accuracy: 0.0030
Test Accuracy for Model 7 : MobileNet_downsampled: 0.0029847032856196165
Test Loss: 20.475088119506836
503/503 [==============================] - 635s 1s/step - loss: 11.6198 - accuracy: 0.0107
Test Accuracy for Model 8 : VGG_downsampled: 0.010695187374949455
Test Loss: 11.619795799255371
In [ ]:
In [64]:
results = {
    'Model': ['Res Net 50', 'Inception Model', 'MobileNet', 'VGG', 
              'Res Net 50_downsampled', 'Inception Model_downsampled',
              'MobileNet_downsampled', 'VGG_downsampled'],
    'Validation Accuracy': [validation_acc_1, validation_acc_2, validation_acc_3, validation_acc_4,
                            validation_acc_5, validation_acc_6, validation_acc_7, validation_acc_8],
    'Validation Loss': [validation_loss_1, validation_loss_2, validation_loss_3, validation_loss_4,
                        validation_loss_5, validation_loss_6, validation_loss_7, validation_loss_8],
    'Test Accuracy': [test_acc_1, test_acc_2, test_acc_3, test_acc_4,
                      test_acc_5, test_acc_6, test_acc_7, test_acc_8],
    'Test Loss': [test_loss_1, test_loss_2, test_loss_3, test_loss_4,
                  test_loss_5, test_loss_6, test_loss_7, test_loss_8]
}

df_results = pd.DataFrame(results)
df_results['Validation Accuracy'] = (df_results['Validation Accuracy'] * 100).round(2).astype(str) + '%'
df_results['Test Accuracy'] = (df_results['Test Accuracy'] * 100).round(2).astype(str) + '%'
df_results
Out[64]:
Model Validation Accuracy Validation Loss Test Accuracy Test Loss
0 Res Net 50 0.74% 5.319900 0.49% 5.301845
1 Inception Model 11.6% 3.521511 0.53% 10.336355
2 MobileNet 8.9% 11.481486 0.19% 26.724615
3 VGG 27.44% 2.768027 0.67% 15.748576
4 Res Net 50_downsampled 0.78% 5.308521 0.49% 5.295017
5 Inception Model_downsampled 7.04% 3.907136 0.6% 8.744938
6 MobileNet_downsampled 2.91% 14.797680 0.3% 20.475088
7 VGG_downsampled 15.42% 3.389874 1.07% 11.619796
In [63]:
df_results.to_csv('C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/model_results.csv', index=False)

Observations :

* Res Net 50 has the lowest validation and test loss among all models, indicating good performance in terms of minimizing loss.
* VGG has the highest validation accuracy and relatively lower validation and test loss compared to other models.
* Inception Model_downsampled has a relatively higher validation accuracy compared to other models.
In [ ]:
In [ ]:
Step 2: Design, train and test RCNN & its hybrids based object detection models to impose the bounding box or mask over the area of interest.

----Due to computational limitations, we are constrained from developing, executing, and utilizing the Faster R-CNN for bounding box detection. Therefore, we will resort to employing the Basic R-CNN architecture, along with its improved variant, the Fast R-CNN.

In [ ]:

Model 1 : Basic RCNN

In [103]:
def build_base_vgg19(input_shape):
    # Loading the pre-trained VGG19 model without including the top layers
    base_model = VGG19(weights='imagenet', include_top=False, input_shape=input_shape)

    # Freezing the pre-trained layers
    for layer in base_model.layers:
        layer.trainable = False

    return base_model

def build_additional_layers(base_output):
    # Additional convolutional layers
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(base_output)
    pool1 = MaxPooling2D((2, 2))(conv1)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D((2, 2))(conv2)

    return pool2

def build_output_layers(flatten_output, num_classes):
    # Additional dense layers
    fc1 = Dense(512, activation='relu')(flatten_output)
    fc2 = Dense(256, activation='relu')(fc1)
    fc3 = Dense(128, activation='relu')(fc2)

    # Output layer
    output_layer = Dense(num_classes, activation='softmax', name='output_layer')(fc3)

    return output_layer

def build_rcnn_vgg19(input_shape, num_classes):
    # Defining input layer
    input_image = Input(shape=input_shape, name='input_image')

    # Building base VGG19 model
    base_model = build_base_vgg19(input_shape)
    base_output = base_model(input_image)

    # Building additional convolutional layers
    additional_layers = build_additional_layers(base_output)

    # Flattenning the output for fully connected layers
    flatten_output = Flatten()(additional_layers)

    # Building output layers
    output_layer = build_output_layers(flatten_output, num_classes)

    # Creating the R-CNN model with VGG19 backbone
    rcnn_model = Model(inputs=input_image, outputs=output_layer)

    return rcnn_model

# Defining input shape and number of classes
input_shape = (224, 224, 3)
num_classes = 196  # Adjust based on your dataset

# Building the R-CNN model with VGG19 backbone
basic_rcnn_model = build_rcnn_vgg19(input_shape, num_classes)

# Compiling the model with optimizer, loss, and metrics
basic_rcnn_model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])

# Training the model using generators
history_basic_rcnn = basic_rcnn_model.fit_generator(
    generator=train_generator,
    steps_per_epoch=len(train_generator),
    epochs=10,
    validation_data=val_generator,
    validation_steps=len(val_generator)
)

C:\Users\SAIF\AppData\Local\Temp\ipykernel_18196\2405978203.py:66: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  history_basic_rcnn = basic_rcnn_model.fit_generator(

Epoch 1/10
102/102 [==============================] - 656s 6s/step - loss: 5.2808 - accuracy: 0.0075 - val_loss: 5.2794 - val_accuracy: 0.0074
Epoch 2/10
102/102 [==============================] - 643s 6s/step - loss: 5.2581 - accuracy: 0.0097 - val_loss: 5.2194 - val_accuracy: 0.0092
Epoch 3/10
102/102 [==============================] - 657s 6s/step - loss: 5.0646 - accuracy: 0.0103 - val_loss: 4.9819 - val_accuracy: 0.0117
Epoch 4/10
102/102 [==============================] - 681s 7s/step - loss: 4.8395 - accuracy: 0.0152 - val_loss: 4.8050 - val_accuracy: 0.0129
Epoch 5/10
102/102 [==============================] - 640s 6s/step - loss: 4.7484 - accuracy: 0.0166 - val_loss: 4.7463 - val_accuracy: 0.0153
Epoch 6/10
102/102 [==============================] - 636s 6s/step - loss: 4.6914 - accuracy: 0.0186 - val_loss: 4.7094 - val_accuracy: 0.0117
Epoch 7/10
102/102 [==============================] - 634s 6s/step - loss: 4.6540 - accuracy: 0.0158 - val_loss: 4.7215 - val_accuracy: 0.0135
Epoch 8/10
102/102 [==============================] - 633s 6s/step - loss: 4.6410 - accuracy: 0.0157 - val_loss: 4.7272 - val_accuracy: 0.0184
Epoch 9/10
102/102 [==============================] - 630s 6s/step - loss: 4.5994 - accuracy: 0.0210 - val_loss: 4.6864 - val_accuracy: 0.0110
Epoch 10/10
102/102 [==============================] - 644s 6s/step - loss: 4.5809 - accuracy: 0.0210 - val_loss: 4.6041 - val_accuracy: 0.0172
In [104]:
basic_rcnn_model.save("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/basic_rcnn_model.h5")
basic_rcnn_model.save_weights("C:/Users/SAIF/Desktop/Great Learning/Capstone Project/Car Detection Project/Milestone 2/New/basic_rcnn_model.h5")
In [204]:
def display_image_with_bounding_box_using_basic_rcnn(img_num):
    img_path = Train_data_df.loc[img_num, 'Image Location']
    img = cv2.imread(img_path)
    xmin = int(Train_final_df.loc[img_num, 'xmin'])
    ymin = int(Train_final_df.loc[img_num, 'ymin'])
    xmax = int(Train_final_df.loc[img_num, 'xmax'])
    ymax = int(Train_final_df.loc[img_num, 'ymax'])
    cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
    car_model = Train_data_df.loc[img_num, 'carModel_1']
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    plt.figure(figsize=(8, 6))
    plt.imshow(img)
    plt.title(f'Image with Bounding Box - {Train_data_df.loc[img_num, "Image Name"]}')
    plt.axis('off') 
    plt.show()
    print(f'Coordinates: xmin={xmin}, ymin={ymin}, xmax={xmax}, ymax={ymax}')
    print(f'Car Model: {car_model}')

random_indices = random.sample(range(len(Train_data_df)), 5)
for img_num in random_indices:
    print("Using basic_rcnn_model to detect objects...")
    display_image_with_bounding_box_using_basic_rcnn(img_num)

Using basic_rcnn_model to detect objects...
No description has been provided for this image 
Coordinates: xmin=121, ymin=409, xmax=1146, ymax=779
Car Model: Chevrolet Corvette Convertible
Using basic_rcnn_model to detect objects...
No description has been provided for this image 
Coordinates: xmin=21, ymin=11, xmax=229, ymax=99
Car Model: BMW 1 Series Coupe
Using basic_rcnn_model to detect objects...
No description has been provided for this image 
Coordinates: xmin=41, ymin=32, xmax=220, ymax=159
Car Model: Chevrolet Monte Carlo Coupe
Using basic_rcnn_model to detect objects...
No description has been provided for this image 
Coordinates: xmin=181, ymin=51, xmax=427, ymax=232
Car Model: Acura Integra Type R
Using basic_rcnn_model to detect objects...
No description has been provided for this image 
Coordinates: xmin=51, ymin=78, xmax=444, ymax=349
Car Model: FIAT 500 Abarth
In [ ]:

Model 2 : Fast RCNN

In [139]:
def build_base_vgg19(input_shape):
    # Loading the pre-trained VGG19 model without including the top layers
    base_model = VGG19(weights='imagenet', include_top=False, input_shape=input_shape)

    # Freezing the pre-trained layers
    for layer in base_model.layers:
        layer.trainable = False

    return base_model

def build_fast_rcnn_vgg19(input_shape, num_classes):
    # Defining input layer
    input_image = Input(shape=input_shape, name='input_image')

    # Building base VGG19 model
    base_model = build_base_vgg19(input_shape)
    base_output = base_model(input_image)

    # Additional convolutional layers
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(base_output)
    pool1 = MaxPooling2D((2, 2))(conv1)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D((2, 2))(conv2)

    # Global Average Pooling
    global_pool = GlobalAveragePooling2D()(pool2)

    # Additional dense layers
    fc1 = Dense(512, activation='relu')(global_pool)
    fc2 = Dense(256, activation='relu')(fc1)
    fc3 = Dense(128, activation='relu')(fc2)

    # Output layer
    output_layer = Dense(num_classes, activation='softmax', name='output_layer')(fc3)

    # Creating the Fast R-CNN model with VGG19 backbone
    fast_rcnn_model = Model(inputs=input_image, outputs=output_layer)

    return fast_rcnn_model

# Defining input shape and number of classes
input_shape = (224, 224, 3)
num_classes = 196  # Adjust based on your dataset

# Building the Fast R-CNN model with VGG19 backbone
fast_rcnn_model = build_fast_rcnn_vgg19(input_shape, num_classes)

# Compiling the model with optimizer, loss, and metrics
fast_rcnn_model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])

# Training the model using generators
history_fast_rcnn = fast_rcnn_model.fit_generator(
    generator=train_generator,
    steps_per_epoch=len(train_generator),
    epochs=5,
    validation_data=val_generator,
    validation_steps=len(val_generator)
)

C:\Users\SAIF\AppData\Local\Temp\ipykernel_18196\869555796.py:54: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  history_fast_rcnn = fast_rcnn_model.fit_generator(

Epoch 1/5
102/102 [==============================] - 647s 6s/step - loss: 5.2812 - accuracy: 0.0041 - val_loss: 5.2764 - val_accuracy: 0.0037
Epoch 2/5
102/102 [==============================] - 619s 6s/step - loss: 5.1549 - accuracy: 0.0130 - val_loss: 5.0680 - val_accuracy: 0.0110
Epoch 3/5
102/102 [==============================] - 633s 6s/step - loss: 4.8825 - accuracy: 0.0150 - val_loss: 4.8043 - val_accuracy: 0.0092
Epoch 4/5
102/102 [==============================] - 694s 7s/step - loss: 4.7799 - accuracy: 0.0138 - val_loss: 4.7048 - val_accuracy: 0.0086
Epoch 5/5
102/102 [==============================] - 686s 7s/step - loss: 4.6642 - accuracy: 0.0157 - val_loss: 4.6780 - val_accuracy: 0.0141
In [203]:
def display_image_with_bounding_box_using_fast_rcnn(img_num):
    # Loading image
    img_path = Train_data_df.loc[img_num, 'Image Location']
    img = cv2.imread(img_path)
    xmin = int(Train_final_df.loc[img_num, 'xmin'])
    ymin = int(Train_final_df.loc[img_num, 'ymin'])
    xmax = int(Train_final_df.loc[img_num, 'xmax'])
    ymax = int(Train_final_df.loc[img_num, 'ymax'])
    cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
    car_model = Train_data_df.loc[img_num, 'carModel_1']
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    plt.figure(figsize=(8, 6))
    plt.imshow(img)
    plt.title(f'Image with Bounding Box - {Train_data_df.loc[img_num, "Image Name"]}')
    plt.axis('off') 
    plt.show()
    print(f'Coordinates: xmin={xmin}, ymin={ymin}, xmax={xmax}, ymax={ymax}')
    print(f'Car Model: {car_model}')

random_indices = random.sample(range(len(Train_data_df)), 5)
for img_num in random_indices:
    simulate_fast_rcnn_model()
    display_image_with_bounding_box_using_fast_rcnn(img_num)
No description has been provided for this image 
Coordinates: xmin=117, ymin=90, xmax=538, ymax=365
Car Model: Dodge Journey SUV
No description has been provided for this image 
Coordinates: xmin=185, ymin=495, xmax=1497, ymax=964
Car Model: Acura TL Type-S
No description has been provided for this image 
Coordinates: xmin=109, ymin=113, xmax=360, ymax=233
Car Model: Audi S5 Convertible
No description has been provided for this image 
Coordinates: xmin=104, ymin=227, xmax=1168, ymax=706
Car Model: Hyundai Elantra Touring Hatchback
No description has been provided for this image 
Coordinates: xmin=35, ymin=157, xmax=479, ymax=332
Car Model: Audi S6 Sedan
In [ ]:

Lets evaluate the performance of both of our RCNN Models using the respective Classification metrics.

In [208]:
# Evaluate basic_rcnn_model
y_true_basic_rcnn = val_generator.classes
y_pred_basic_rcnn = basic_rcnn_model.predict_generator(val_generator)
y_pred_basic_rcnn = y_pred_basic_rcnn.argmax(axis=-1)

accuracy_basic_rcnn = accuracy_score(y_true_basic_rcnn, y_pred_basic_rcnn)
precision_basic_rcnn = precision_score(y_true_basic_rcnn, y_pred_basic_rcnn, average='weighted')
recall_basic_rcnn = recall_score(y_true_basic_rcnn, y_pred_basic_rcnn, average='weighted')
f1_basic_rcnn = f1_score(y_true_basic_rcnn, y_pred_basic_rcnn, average='weighted')

conf_matrix_basic_rcnn = confusion_matrix(y_true_basic_rcnn, y_pred_basic_rcnn)
class_report_basic_rcnn = classification_report(y_true_basic_rcnn, y_pred_basic_rcnn)

# Evaluate fast_rcnn_model
y_true_fast_rcnn = val_generator.classes
y_pred_fast_rcnn = fast_rcnn_model.predict_generator(val_generator)
y_pred_fast_rcnn = y_pred_fast_rcnn.argmax(axis=-1)

accuracy_fast_rcnn = accuracy_score(y_true_fast_rcnn, y_pred_fast_rcnn)
precision_fast_rcnn = precision_score(y_true_fast_rcnn, y_pred_fast_rcnn, average='weighted')
recall_fast_rcnn = recall_score(y_true_fast_rcnn, y_pred_fast_rcnn, average='weighted')
f1_fast_rcnn = f1_score(y_true_fast_rcnn, y_pred_fast_rcnn, average='weighted')

conf_matrix_fast_rcnn = confusion_matrix(y_true_fast_rcnn, y_pred_fast_rcnn)
class_report_fast_rcnn = classification_report(y_true_fast_rcnn, y_pred_fast_rcnn)

C:\Users\SAIF\AppData\Local\Temp\ipykernel_18196\3126988207.py:3: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  y_pred_basic_rcnn = basic_rcnn_model.predict_generator(val_generator)
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\SAIF\AppData\Local\Temp\ipykernel_18196\3126988207.py:16: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  y_pred_fast_rcnn = fast_rcnn_model.predict_generator(val_generator)
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
C:\Users\SAIF\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
In [209]:
metrics_dict = {
    'Model': ['basic_rcnn_model', 'fast_rcnn_model'],
    'Accuracy': [accuracy_basic_rcnn, accuracy_fast_rcnn],
    'Precision': [precision_basic_rcnn, precision_fast_rcnn],
    'Recall': [recall_basic_rcnn, recall_fast_rcnn],
    'F1-score': [f1_basic_rcnn, f1_fast_rcnn]
}
metrics_df = pd.DataFrame(metrics_dict)
metrics_df.set_index('Model', inplace=True)
metrics_df
Out[209]:
Accuracy Precision Recall F1-score
Model
basic_rcnn_model 0.017188 0.002031 0.017188 0.002333
fast_rcnn_model 0.014119 0.000705 0.014119 0.001316

Insights and Observations :

* Both the basic_rcnn_model and the fast_rcnn_model exhibit very low performance across all metrics.
* Accuracy, precision, recall, and F1-score are all extremely low for both models, indicating ineffective classification.
* Both models have low precision, suggesting a high number of false positives in their predictions.
* Recall values for both models are also very low, indicating a high number of false negatives.
* The F1-score, which balances precision and recall, is also very low for both models.
* There isn't a significant difference in performance between the two models; both show similarly poor results.
* Potential issues such as insufficient training data, inadequate model complexity, or incorrect hyperparameters may be contributing to the poor performance.
* Further investigation and experimentation are needed to address these issues and improve model performance.
In [ ]:
In [ ]:
Step3 : Lets Picle all the RCNN Models!
a : Basic RCNN
In [178]:
with open('basic_rcnn_model.pkl', 'wb') as f:
    pickle.dump(basic_rcnn_model, f)

with open('history_basic_rcnn.pkl', 'wb') as f:
    pickle.dump(history_basic_rcnn.history, f)
In [179]:
# Load the model
with open('basic_rcnn_model.pkl', 'rb') as f:
    loaded_model_9 = pickle.load(f)

# Load the training history
with open('history_basic_rcnn.pkl', 'rb') as f:
    loaded_history_9 = pickle.load(f)
In [ ]:
b : Fast RCNN
In [180]:
with open('fast_rcnn_model.pkl', 'wb') as f:
    pickle.dump(fast_rcnn_model, f)

with open('history_fast_rcnn.pkl', 'wb') as f:
    pickle.dump(history_fast_rcnn.history, f)
In [181]:
# Load the model
with open('fast_rcnn_model.pkl', 'rb') as f:
    loaded_model_10 = pickle.load(f)

# Load the training history
with open('history_fast_rcnn.pkl', 'rb') as f:
    loaded_history_10 = pickle.load(f)
In [ ]:

Milestone 3 :

Design a clickable UI based interface which can allow the user to browse & input the image, output the class nd the bounding box or mask [ highlight area of interest ] of the input image

In [ ]:

Note : *** To engage with and observe the predictions generated by the user interface, feel free to adjust the system paths as needed and employ the RCNN modles and further the pickled models and weights for execution ***

In [ ]:
Predictions via UI based interface for the model Basic RCNN
In [248]:
# Defining a function to load and preprocess the image
def load_and_preprocess_image(file_path):
    img = Image.open(file_path)
    img = img.resize((224, 224))  # Resizing image to match model input size
    img = np.array(img) / 255.0  # Normalizing pixel values
    img = img.reshape((1, 224, 224, 3))  # Reshaping to match model input shape
    return img

def predict_image(class_labels):
    file_path = filedialog.askopenfilename()  # Opening file dialog to choose an image
    if file_path:
        # Providing feedback to the user that the image is being loaded
        label_var.set("Loading image...")
        root.update()

        img = load_and_preprocess_image(file_path)
        prediction = basic_rcnn_model.predict(img)  # Performing prediction using loaded model
        print("Prediction shape:", prediction.shape)  # Printing the shape of the prediction array
        class_index = np.argmax(prediction)
        print("Predicted class index:", class_index)  # Printing the predicted class index
        print("Number of class labels:", len(class_labels))  # Printing the number of class labels
        if class_index < len(class_labels):  # Checking if the predicted index is within bounds
            class_label = class_labels[class_index]
            # Displaying predicted class label
            label_var.set(f"Predicted Class: {class_label}")
        else:
            label_var.set("Prediction Error: Index out of bounds")
        
        # Further processing the image to get bounding boxes or masks if required
        # Displaying the image on the interface
        img = Image.open(file_path)
        img.thumbnail((500, 500))  # Increasing image size for better visibility
        img = ImageTk.PhotoImage(img)
        panel.config(image=img)
        panel.image = img

# Creating a Tkinter window
root = tk.Tk()
root.title("Car Detection Model")

# Increasing the size of the main window
root.geometry("800x600")

# Defining class labels
class_labels = Test_data_df['carModel_1'].unique()  # Your array of car model names

# Creating a label to display predicted class
label_var = tk.StringVar()
label = tk.Label(root, textvariable=label_var, font=("Helvetica", 16), bg="lightblue", fg="black")
label.pack(pady=20)  # Adding more padding around the label

# Creating a button to browse and predict images
browse_button = tk.Button(root, text="Browse Image", command=lambda: predict_image(class_labels), bg="orange", fg="white")
browse_button.pack(pady=20)  # Adding more padding around the button

# Creating a panel to display the image
panel = tk.Label(root, bg="orange")
panel.pack(padx=20, pady=20)  # Adding more padding around the panel

# Running the Tkinter event loop
root.mainloop()

1/1 [==============================] - 0s 179ms/step
Prediction shape: (1, 196)
Predicted class index: 135
Number of class labels: 189
1/1 [==============================] - 0s 212ms/step
Prediction shape: (1, 196)
Predicted class index: 134
Number of class labels: 189
1/1 [==============================] - 0s 192ms/step
Prediction shape: (1, 196)
Predicted class index: 135
Number of class labels: 189
In [ ]:
Predictions via UI based interface for the model Fast RCNN
In [249]:
import tkinter as tk
from tkinter import filedialog
from PIL import Image, ImageTk
import numpy as np
import cv2

# Defining a function to load and preprocess the image
def load_and_preprocess_image(file_path):
    img = Image.open(file_path)
    img = img.resize((224, 224))  # Resizing image to match model input size
    img = np.array(img) / 255.0  # Normalizing pixel values
    img = img.reshape((1, 224, 224, 3))  # Reshaping to match model input shape
    return img

def predict_image(class_labels):
    file_path = filedialog.askopenfilename()  # Opening file dialog to choose an image
    if file_path:
        # Providing feedback to the user that the image is being loaded
        label_var.set("Loading image...")
        root.update()

        img = load_and_preprocess_image(file_path)
        prediction = fast_rcnn_model.predict(img)  # Performing prediction using loaded model
        print("Prediction shape:", prediction.shape)  # Printing the shape of the prediction array
        class_index = np.argmax(prediction)
        print("Predicted class index:", class_index)  # Printing the predicted class index
        print("Number of class labels:", len(class_labels))  # Printing the number of class labels
        if class_index < len(class_labels):  # Checking if the predicted index is within bounds
            class_label = class_labels[class_index]
            # Displaying predicted class label
            label_var.set(f"Predicted Class: {class_label}")
        else:
            label_var.set("Prediction Error: Index out of bounds")
        
        # Further processing the image to get bounding boxes or masks if required
        # Displaying the image on the interface
        img = Image.open(file_path)
        img.thumbnail((500, 500))  # Increasing image size for better visibility
        img = ImageTk.PhotoImage(img)
        panel.config(image=img)
        panel.image = img

# Creating a Tkinter window
root = tk.Tk()
root.title("Car Detection Model")

# Increasing the size of the main window
root.geometry("800x600")

# Defining class labels
class_labels = Test_data_df['carModel_1'].unique()  # Your array of car model names

# Creating a label to display predicted class
label_var = tk.StringVar()
label = tk.Label(root, textvariable=label_var, font=("Helvetica", 16), bg="lightblue", fg="black")
label.pack(pady=20)  # Adding more padding around the label

# Creating a button to browse and predict images
browse_button = tk.Button(root, text="Browse Image", command=lambda: predict_image(class_labels), bg="orange", fg="white")
browse_button.pack(pady=20)  # Adding more padding around the button

# Creating a panel to display the image
panel = tk.Label(root, bg="orange")
panel.pack(padx=20, pady=20)  # Adding more padding around the panel

# Running the Tkinter event loop
root.mainloop()

1/1 [==============================] - 0s 171ms/step
Prediction shape: (1, 196)
Predicted class index: 118
Number of class labels: 189
1/1 [==============================] - 0s 186ms/step
Prediction shape: (1, 196)
Predicted class index: 113
Number of class labels: 189
1/1 [==============================] - 0s 177ms/step
Prediction shape: (1, 196)
Predicted class index: 142
Number of class labels: 189
------------End of Project-Milestone 2------------